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zulonas/lab32.ipynb

Created May 20, 2021 14:43
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lab32.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "lab32.ipynb",
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyPp2QxQ8tjfsDifNIUvliE7",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/zulonas/3ac13a588cb38fb96777230d61b0ce19/lab32.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "_H_O6y--qKf0"
},
"source": [
"import math\n",
"import keras\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.model_selection import train_test_split, KFold, cross_val_score\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense\n",
"from keras.wrappers.scikit_learn import KerasRegressor"
],
"execution_count": 196,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "kc1d7HZPtqp4"
},
"source": [
"1. Pasirinkite tikslo atributą iš 1 laboratorinio darbo duomenų rinkinio (jei tikslo atributas nebuvo apibrėžtas). (Pastaba: pavyzdžiui, banko klientų duomenų rinkinyje tikslo atributu gali būti laikomi kliento mokumo lygis arba kredito reitingas, filmų duomenų rinkinyje tikslo atributu gali būti sugeneruotas pelnas).\n",
"2. Jei reikia, atlikite tikslinių atributų reikšmių pertvarkymus (pvz., platus skaitinių atributų verčių\n",
"diapazonas keičiamas mažesniu (kategorinių) intervalų skaičiumi (pvz., prognozuojamų reikšmių\n",
"diapazoną 1..2000 galima pakeisti 1...5 intervalais).\n",
"3. Sukurkite reikšmės prognozavimo ar klasifikacijos modelį. Python mokomoji medžiaga pateikta\n",
"adresu https://iamtrask.github.io/2015/07/12/basic-python-network/\n",
"4. Įvertinkite sukurto modelio vidutinį tikslumo įvertį, taikant 10 intervalų kryžminės patikros metodą.\n",
"5. Pritaikykite bet kurią iš priemonių (pavyzdžiai pateikiami žemiau), kad padidintumėte vidutinį\n",
"tikslumą bent 5 procentais ir pakartokite 4-ą darbo eigos žingsnį:\n",
" - Pertvarkyti duomenų rinkinį\n",
" - Pakeiskite mokymosi greitį\n",
" - Pakeiskite aktyvacijos funkciją\n",
" - Pakeisti dirbtinio neuronų tinklo (DNT) struktūrą"
]
},
{
"cell_type": "code",
"metadata": {
"id": "-zCtsya5UZB2"
},
"source": [
"data = pd.read_csv('https://storage.googleapis.com/mledu-datasets/california_housing_train.csv')"
],
"execution_count": 197,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297
},
"id": "zzs33z5LUZ47",
"outputId": "980dd347-d611-4c2d-bd0e-7e7c18b61193"
},
"source": [
"data.describe()"
],
"execution_count": 198,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>longitude</th>\n",
" <th>latitude</th>\n",
" <th>housing_median_age</th>\n",
" <th>total_rooms</th>\n",
" <th>total_bedrooms</th>\n",
" <th>population</th>\n",
" <th>households</th>\n",
" <th>median_income</th>\n",
" <th>median_house_value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>17000.000000</td>\n",
" <td>17000.000000</td>\n",
" <td>17000.000000</td>\n",
" <td>17000.000000</td>\n",
" <td>17000.000000</td>\n",
" <td>17000.000000</td>\n",
" <td>17000.000000</td>\n",
" <td>17000.000000</td>\n",
" <td>17000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>-119.562108</td>\n",
" <td>35.625225</td>\n",
" <td>28.589353</td>\n",
" <td>2643.664412</td>\n",
" <td>539.410824</td>\n",
" <td>1429.573941</td>\n",
" <td>501.221941</td>\n",
" <td>3.883578</td>\n",
" <td>207300.912353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>2.005166</td>\n",
" <td>2.137340</td>\n",
" <td>12.586937</td>\n",
" <td>2179.947071</td>\n",
" <td>421.499452</td>\n",
" <td>1147.852959</td>\n",
" <td>384.520841</td>\n",
" <td>1.908157</td>\n",
" <td>115983.764387</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-124.350000</td>\n",
" <td>32.540000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.499900</td>\n",
" <td>14999.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>-121.790000</td>\n",
" <td>33.930000</td>\n",
" <td>18.000000</td>\n",
" <td>1462.000000</td>\n",
" <td>297.000000</td>\n",
" <td>790.000000</td>\n",
" <td>282.000000</td>\n",
" <td>2.566375</td>\n",
" <td>119400.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>-118.490000</td>\n",
" <td>34.250000</td>\n",
" <td>29.000000</td>\n",
" <td>2127.000000</td>\n",
" <td>434.000000</td>\n",
" <td>1167.000000</td>\n",
" <td>409.000000</td>\n",
" <td>3.544600</td>\n",
" <td>180400.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>-118.000000</td>\n",
" <td>37.720000</td>\n",
" <td>37.000000</td>\n",
" <td>3151.250000</td>\n",
" <td>648.250000</td>\n",
" <td>1721.000000</td>\n",
" <td>605.250000</td>\n",
" <td>4.767000</td>\n",
" <td>265000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>-114.310000</td>\n",
" <td>41.950000</td>\n",
" <td>52.000000</td>\n",
" <td>37937.000000</td>\n",
" <td>6445.000000</td>\n",
" <td>35682.000000</td>\n",
" <td>6082.000000</td>\n",
" <td>15.000100</td>\n",
" <td>500001.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" longitude latitude ... median_income median_house_value\n",
"count 17000.000000 17000.000000 ... 17000.000000 17000.000000\n",
"mean -119.562108 35.625225 ... 3.883578 207300.912353\n",
"std 2.005166 2.137340 ... 1.908157 115983.764387\n",
"min -124.350000 32.540000 ... 0.499900 14999.000000\n",
"25% -121.790000 33.930000 ... 2.566375 119400.000000\n",
"50% -118.490000 34.250000 ... 3.544600 180400.000000\n",
"75% -118.000000 37.720000 ... 4.767000 265000.000000\n",
"max -114.310000 41.950000 ... 15.000100 500001.000000\n",
"\n",
"[8 rows x 9 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 198
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 516
},
"id": "EjQNwqCcAeAH",
"outputId": "b013fea2-f85a-48e1-dbca-75981fb3f414"
},
"source": [
"plt.figure()\n",
"data.hist(bins=50, figsize=(16,8))\n",
"plt.show()"
],
"execution_count": 199,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 1152x576 with 9 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "YGoj2O8eqURl"
},
"source": [
"X = data.drop(columns=['median_house_value', 'longitude', 'latitude'])\n",
"y = data['median_house_value']\n",
"\n",
"X = np.array(X)\n",
"y = np.array(y)"
],
"execution_count": 200,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 265
},
"id": "hmnfTViRBgHQ",
"outputId": "53e5830b-91ae-4ac5-e653-019aaca802ba"
},
"source": [
"plt.figure()\n",
"plt.plot(X)\n",
"plt.show()"
],
"execution_count": 201,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "4NFpnEsMsmGc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 265
},
"outputId": "68044879-bee9-4035-f511-080f3534d0bc"
},
"source": [
"# Standartization\n",
"X_std = np.copy(X)\n",
"\n",
"for i in range(0, 6):\n",
" X_std[:, i] = (X_std[:, i] - X_std[:, i].mean()) / X_std[:, i].std()\n",
"\n",
"plt.figure()\n",
"plt.plot(X_std)\n",
"plt.show()"
],
"execution_count": 202,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "okStk7qxNDG2"
},
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X_std, y, test_size=0.3, random_state=42)"
],
"execution_count": 203,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zOuEDpR4ggaq",
"outputId": "2ab69f50-e920-4249-bc58-d11dc24e0e09"
},
"source": [
"def make_classifier():\n",
" classifier = Sequential()\n",
" classifier.add(Dense(3, kernel_initializer = 'normal', activation = 'relu', input_dim = 6))\n",
" classifier.add(Dense(1, kernel_initializer = 'normal'))\n",
" classifier.compile(optimizer= 'adam', loss = 'mean_squared_error')\n",
" return classifier\n",
"\n",
"make_classifier().summary()\n",
"print(make_classifier().get_weights())"
],
"execution_count": 204,
"outputs": [
{
"output_type": "stream",
"text": [
"Model: \"sequential_164\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_328 (Dense) (None, 3) 21 \n",
"_________________________________________________________________\n",
"dense_329 (Dense) (None, 1) 4 \n",
"=================================================================\n",
"Total params: 25\n",
"Trainable params: 25\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"[array([[-0.08211987, 0.09985056, -0.00533692],\n",
" [ 0.08427793, 0.02890524, 0.05158813],\n",
" [ 0.00211019, -0.034972 , -0.04090815],\n",
" [ 0.11112738, 0.00880713, 0.06004889],\n",
" [ 0.00412849, -0.01547211, 0.03698185],\n",
" [ 0.00387961, 0.01784034, -0.04248256]], dtype=float32), array([0., 0., 0.], dtype=float32), array([[-0.04004624],\n",
" [ 0.00190758],\n",
" [-0.05348462]], dtype=float32), array([0.], dtype=float32)]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "oW4K7FFzMpZH",
"outputId": "5999999e-04e7-482f-bff3-ce72576ff7c6"
},
"source": [
"regressor = KerasRegressor(build_fn=make_classifier, nb_epoch=1000, batch_size=10)\n",
"regressor.fit(X_train, y_train)"
],
"execution_count": 205,
"outputs": [
{
"output_type": "stream",
"text": [
"1190/1190 [==============================] - 1s 891us/step - loss: 54815568071.4694\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x7f29f16f5dd0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 205
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "a9J6qD27WAlS",
"outputId": "a17f3fa9-bb62-41c2-9079-ac2019db9170"
},
"source": [
"make_classifier().get_weights()"
],
"execution_count": 206,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[array([[-0.09521233, -0.05778117, 0.03947655],\n",
" [ 0.04262883, -0.06259254, 0.00380649],\n",
" [-0.03634699, 0.06141048, -0.09260476],\n",
" [-0.15431689, 0.02168981, 0.04822403],\n",
" [-0.01684596, -0.0313106 , -0.02027882],\n",
" [ 0.01559381, 0.0252065 , -0.06454287]], dtype=float32),\n",
" array([0., 0., 0.], dtype=float32),\n",
" array([[-0.02632541],\n",
" [-0.00964384],\n",
" [-0.06122212]], dtype=float32),\n",
" array([0.], dtype=float32)]"
]
},
"metadata": {
"tags": []
},
"execution_count": 206
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "c2FILDR5jD-C",
"outputId": "d903000d-1bac-44aa-9f56-04c9bb7475bf"
},
"source": [
"y_pred = regressor.predict(X_test)\n",
"\n",
"def get_mse(initial, predicted):\n",
" err = 0.0\n",
" for i in range(len(initial)):\n",
" err += (predicted[i] - initial[i]) ** 2\n",
" return err / len(predicted)\n",
"\n",
"def get_mad(predicted):\n",
" return np.median(np.absolute(predicted - np.median(predicted)))\n",
"\n",
"mse = get_mse(y_test, y_pred)\n",
"mad = get_mad(y_pred)\n",
"\n",
"print(\"MSE = %f\" % (mse))\n",
"print(\"MAD = %f\" % (mad))"
],
"execution_count": 209,
"outputs": [
{
"output_type": "stream",
"text": [
"MSE = 58202736169.152023\n",
"MAD = 5.913820\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 265
},
"collapsed": true,
"id": "O3mqQOP9QGJp",
"outputId": "9fc61f23-21d3-43b5-9e72-d101930d9437"
},
"source": [
"plt.figure()\n",
"\n",
"y_pred1 = (y_pred - y_pred.mean()) / y_pred.std()\n",
"y_test1 = (y_test - y_test.mean()) / y_test.std()\n",
"\n",
"plt.plot(y_pred1[:100], label=\"Gauta (prognozuota) reikšmė\")\n",
"plt.plot(y_test1[:100], label=\"Pradiniai (testiniai) duomenys\")\n",
"\n",
"plt.legend()\n",
"plt.show()"
],
"execution_count": 213,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ii9VqWGSmtN4",
"outputId": "19dfa8e7-5485-4f74-a361-0c3eb4b66004"
},
"source": [
"estimator = KerasRegressor(build_fn=make_classifier, epochs=10, batch_size=10, verbose=1)\n",
"kfold = KFold(n_splits=10)\n",
"results = cross_val_score(estimator, X_train, y_train, cv=kfold)\n",
"print(\"Results: %.2f (%.2f) MSE\" % (results.mean(), results.std()))\n"
],
"execution_count": 187,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"1071/1071 [==============================] - 1s 955us/step - loss: 54996066670.8060\n",
"Epoch 2/10\n",
"1071/1071 [==============================] - 1s 932us/step - loss: 55662186060.4179\n",
"Epoch 3/10\n",
"1071/1071 [==============================] - 1s 953us/step - loss: 56319307619.3433\n",
"Epoch 4/10\n",
"1071/1071 [==============================] - 1s 946us/step - loss: 54847246836.5373\n",
"Epoch 5/10\n",
"1071/1071 [==============================] - 1s 996us/step - loss: 55714457941.9701\n",
"Epoch 6/10\n",
"1071/1071 [==============================] - 1s 1ms/step - loss: 55719915095.8806\n",
"Epoch 7/10\n",
"1071/1071 [==============================] - 1s 979us/step - loss: 54957287129.7910\n",
"Epoch 8/10\n",
"1071/1071 [==============================] - 1s 930us/step - loss: 54835031259.7015\n",
"Epoch 9/10\n",
"1071/1071 [==============================] - 1s 930us/step - loss: 55282646806.9254\n",
"Epoch 10/10\n",
"1071/1071 [==============================] - 1s 924us/step - loss: 55838272114.6269\n",
"119/119 [==============================] - 0s 832us/step - loss: 54832996352.0000\n",
"Epoch 1/10\n",
"1071/1071 [==============================] - 1s 890us/step - loss: 56383452817.1940\n",
"Epoch 2/10\n",
"1071/1071 [==============================] - 1s 931us/step - loss: 55844543029.4925\n",
"Epoch 3/10\n",
"1071/1071 [==============================] - 1s 929us/step - loss: 55364479281.6716\n",
"Epoch 4/10\n",
"1071/1071 [==============================] - 1s 933us/step - loss: 55216567808.0000\n",
"Epoch 5/10\n",
"1071/1071 [==============================] - 1s 930us/step - loss: 55387290834.1493\n",
"Epoch 6/10\n",
"1071/1071 [==============================] - 1s 936us/step - loss: 54662843965.1343\n",
"Epoch 7/10\n",
"1071/1071 [==============================] - 1s 914us/step - loss: 55759364699.7015\n",
"Epoch 8/10\n",
"1071/1071 [==============================] - 1s 927us/step - loss: 54894335438.3284\n",
"Epoch 9/10\n",
"1071/1071 [==============================] - 1s 898us/step - loss: 56241406922.5075\n",
"Epoch 10/10\n",
"1071/1071 [==============================] - 1s 911us/step - loss: 55466686819.3433\n",
"119/119 [==============================] - 0s 768us/step - loss: 55414632448.0000\n",
"Epoch 1/10\n",
"1071/1071 [==============================] - 1s 864us/step - loss: 55718542301.6119\n",
"Epoch 2/10\n",
"1071/1071 [==============================] - 1s 920us/step - loss: 54813743031.4030\n",
"Epoch 3/10\n",
"1071/1071 [==============================] - 1s 969us/step - loss: 54522572566.9254\n",
"Epoch 4/10\n",
"1071/1071 [==============================] - 1s 916us/step - loss: 55730919485.1343\n",
"Epoch 5/10\n",
"1071/1071 [==============================] - 1s 892us/step - loss: 55138253082.7463\n",
"Epoch 6/10\n",
"1071/1071 [==============================] - 1s 940us/step - loss: 55309142000.7164\n",
"Epoch 7/10\n",
"1071/1071 [==============================] - 1s 921us/step - loss: 54806629620.5373\n",
"Epoch 8/10\n",
"1071/1071 [==============================] - 1s 935us/step - loss: 55336922857.0746\n",
"Epoch 9/10\n",
"1071/1071 [==============================] - 1s 966us/step - loss: 54536274653.6119\n",
"Epoch 10/10\n",
"1071/1071 [==============================] - 1s 948us/step - loss: 57365896256.9552\n",
"119/119 [==============================] - 0s 784us/step - loss: 58834325504.0000\n",
"Epoch 1/10\n",
"1071/1071 [==============================] - 1s 844us/step - loss: 56510689665.9104\n",
"Epoch 2/10\n",
"1071/1071 [==============================] - 1s 894us/step - loss: 56288758101.9701\n",
"Epoch 3/10\n",
"1071/1071 [==============================] - 1s 941us/step - loss: 55452443233.4328\n",
"Epoch 4/10\n",
"1071/1071 [==============================] - 1s 893us/step - loss: 53919713486.3284\n",
"Epoch 5/10\n",
"1071/1071 [==============================] - 1s 929us/step - loss: 56137328957.1343\n",
"Epoch 6/10\n",
"1071/1071 [==============================] - 1s 934us/step - loss: 55167042491.2239\n",
"Epoch 7/10\n",
"1071/1071 [==============================] - 1s 977us/step - loss: 56000412545.9104\n",
"Epoch 8/10\n",
"1071/1071 [==============================] - 1s 964us/step - loss: 54874972672.0000\n",
"Epoch 9/10\n",
"1071/1071 [==============================] - 1s 960us/step - loss: 55509724083.5821\n",
"Epoch 10/10\n",
"1071/1071 [==============================] - 1s 940us/step - loss: 55686599890.1493\n",
"119/119 [==============================] - 0s 1ms/step - loss: 53877567488.0000\n",
"Epoch 1/10\n",
"1071/1071 [==============================] - 1s 922us/step - loss: 56029327016.1194\n",
"Epoch 2/10\n",
"1071/1071 [==============================] - 1s 979us/step - loss: 55284720403.1045\n",
"Epoch 3/10\n",
"1071/1071 [==============================] - 1s 920us/step - loss: 55695865378.3881\n",
"Epoch 4/10\n",
"1071/1071 [==============================] - 1s 910us/step - loss: 56293840007.6418\n",
"Epoch 5/10\n",
"1071/1071 [==============================] - 1s 877us/step - loss: 55810322133.9701\n",
"Epoch 6/10\n",
"1071/1071 [==============================] - 1s 930us/step - loss: 53981781664.4776\n",
"Epoch 7/10\n",
"1071/1071 [==============================] - 1s 883us/step - loss: 55541290318.3284\n",
"Epoch 8/10\n",
"1071/1071 [==============================] - 1s 892us/step - loss: 55247506863.7612\n",
"Epoch 9/10\n",
"1071/1071 [==============================] - 1s 925us/step - loss: 56324808432.7164\n",
"Epoch 10/10\n",
"1071/1071 [==============================] - 1s 908us/step - loss: 54925540695.8806\n",
"119/119 [==============================] - 0s 775us/step - loss: 55943786496.0000\n",
"Epoch 1/10\n",
"1071/1071 [==============================] - 1s 889us/step - loss: 55897793365.9701\n",
"Epoch 2/10\n",
"1071/1071 [==============================] - 1s 910us/step - loss: 55111885495.4030\n",
"Epoch 3/10\n",
"1071/1071 [==============================] - 1s 927us/step - loss: 56654168740.2985\n",
"Epoch 4/10\n",
"1071/1071 [==============================] - 1s 921us/step - loss: 56941020301.3731\n",
"Epoch 5/10\n",
"1071/1071 [==============================] - 1s 921us/step - loss: 55354673576.1194\n",
"Epoch 6/10\n",
"1071/1071 [==============================] - 1s 932us/step - loss: 55945209431.8806\n",
"Epoch 7/10\n",
"1071/1071 [==============================] - 1s 903us/step - loss: 56042017199.7612\n",
"Epoch 8/10\n",
"1071/1071 [==============================] - 1s 934us/step - loss: 55885961093.7313\n",
"Epoch 9/10\n",
"1071/1071 [==============================] - 1s 892us/step - loss: 56338904222.5672\n",
"Epoch 10/10\n",
"1071/1071 [==============================] - 1s 978us/step - loss: 55512477799.1642\n",
"119/119 [==============================] - 0s 826us/step - loss: 53468233728.0000\n",
"Epoch 1/10\n",
"1071/1071 [==============================] - 1s 906us/step - loss: 55690470587.2239\n",
"Epoch 2/10\n",
"1071/1071 [==============================] - 1s 934us/step - loss: 55451933739.9403\n",
"Epoch 3/10\n",
"1071/1071 [==============================] - 1s 916us/step - loss: 56123108420.7761\n",
"Epoch 4/10\n",
"1071/1071 [==============================] - 1s 932us/step - loss: 55436661473.4328\n",
"Epoch 5/10\n",
"1071/1071 [==============================] - 1s 961us/step - loss: 54774476643.3433\n",
"Epoch 6/10\n",
"1071/1071 [==============================] - 1s 979us/step - loss: 55057844483.8209\n",
"Epoch 7/10\n",
"1071/1071 [==============================] - 1s 887us/step - loss: 55137744265.5522\n",
"Epoch 8/10\n",
"1071/1071 [==============================] - 1s 917us/step - loss: 55291063250.1493\n",
"Epoch 9/10\n",
"1071/1071 [==============================] - 1s 917us/step - loss: 55581354182.6866\n",
"Epoch 10/10\n",
"1071/1071 [==============================] - 1s 918us/step - loss: 54540807767.8806\n",
"119/119 [==============================] - 0s 813us/step - loss: 56527781888.0000\n",
"Epoch 1/10\n",
"1071/1071 [==============================] - 1s 879us/step - loss: 55278571229.6119\n",
"Epoch 2/10\n",
"1071/1071 [==============================] - 1s 873us/step - loss: 56005826796.8955\n",
"Epoch 3/10\n",
"1071/1071 [==============================] - 1s 954us/step - loss: 54743946366.0896\n",
"Epoch 4/10\n",
"1071/1071 [==============================] - 1s 910us/step - loss: 55518902331.2239\n",
"Epoch 5/10\n",
"1071/1071 [==============================] - 1s 918us/step - loss: 55441469887.0448\n",
"Epoch 6/10\n",
"1071/1071 [==============================] - 1s 909us/step - loss: 55674925950.0896\n",
"Epoch 7/10\n",
"1071/1071 [==============================] - 1s 923us/step - loss: 55997104213.9701\n",
"Epoch 8/10\n",
"1071/1071 [==============================] - 1s 902us/step - loss: 55633986659.3433\n",
"Epoch 9/10\n",
"1071/1071 [==============================] - 1s 902us/step - loss: 55611993126.2090\n",
"Epoch 10/10\n",
"1071/1071 [==============================] - 1s 991us/step - loss: 55878931941.2537\n",
"119/119 [==============================] - 0s 764us/step - loss: 54763802624.0000\n",
"Epoch 1/10\n",
"1071/1071 [==============================] - 1s 893us/step - loss: 56632298255.2836\n",
"Epoch 2/10\n",
"1071/1071 [==============================] - 1s 922us/step - loss: 54929606673.1940\n",
"Epoch 3/10\n",
"1071/1071 [==============================] - 1s 906us/step - loss: 56004126355.1045\n",
"Epoch 4/10\n",
"1071/1071 [==============================] - 1s 927us/step - loss: 55149424269.3731\n",
"Epoch 5/10\n",
"1071/1071 [==============================] - 1s 906us/step - loss: 56291820723.5821\n",
"Epoch 6/10\n",
"1071/1071 [==============================] - 1s 944us/step - loss: 55978992739.3433\n",
"Epoch 7/10\n",
"1071/1071 [==============================] - 1s 922us/step - loss: 55650399980.8955\n",
"Epoch 8/10\n",
"1071/1071 [==============================] - 1s 974us/step - loss: 55391987203.8209\n",
"Epoch 9/10\n",
"1071/1071 [==============================] - 1s 875us/step - loss: 56141172904.1194\n",
"Epoch 10/10\n",
"1071/1071 [==============================] - 1s 904us/step - loss: 55651507570.6269\n",
"119/119 [==============================] - 0s 951us/step - loss: 55259987968.0000\n",
"Epoch 1/10\n",
"1071/1071 [==============================] - 1s 860us/step - loss: 54575625578.9851\n",
"Epoch 2/10\n",
"1071/1071 [==============================] - 1s 875us/step - loss: 56618096143.2836\n",
"Epoch 3/10\n",
"1071/1071 [==============================] - 1s 917us/step - loss: 55191909395.1045\n",
"Epoch 4/10\n",
"1071/1071 [==============================] - 1s 887us/step - loss: 55873949638.6866\n",
"Epoch 5/10\n",
"1071/1071 [==============================] - 1s 935us/step - loss: 55559995254.4478\n",
"Epoch 6/10\n",
"1071/1071 [==============================] - 1s 926us/step - loss: 55969407919.7612\n",
"Epoch 7/10\n",
"1071/1071 [==============================] - 1s 912us/step - loss: 56927552294.2090\n",
"Epoch 8/10\n",
"1071/1071 [==============================] - 1s 954us/step - loss: 55602854403.8209\n",
"Epoch 9/10\n",
"1071/1071 [==============================] - 1s 940us/step - loss: 54852669795.3433\n",
"Epoch 10/10\n",
"1071/1071 [==============================] - 1s 951us/step - loss: 55619118523.2239\n",
"119/119 [==============================] - 0s 846us/step - loss: 54641139712.0000\n",
"Results: -55356425420.80 (1439258430.71) MSE\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8z-lfQsid1NM",
"outputId": "330801d9-6daa-4a63-9dc5-887fdabf2806"
},
"source": [
"estimator.fit(X_train, y_train)\n",
"prediction = estimator.predict(X_test)"
],
"execution_count": 188,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"1190/1190 [==============================] - 1s 911us/step - loss: 55127846086.6096\n",
"Epoch 2/10\n",
"1190/1190 [==============================] - 1s 862us/step - loss: 54856059187.8018\n",
"Epoch 3/10\n",
"1190/1190 [==============================] - 1s 892us/step - loss: 54981760994.7674\n",
"Epoch 4/10\n",
"1190/1190 [==============================] - 1s 889us/step - loss: 54843262506.5592\n",
"Epoch 5/10\n",
"1190/1190 [==============================] - 1s 863us/step - loss: 55705735116.4131\n",
"Epoch 6/10\n",
"1190/1190 [==============================] - 1s 884us/step - loss: 55315307295.5970\n",
"Epoch 7/10\n",
"1190/1190 [==============================] - 1s 893us/step - loss: 55891650018.3375\n",
"Epoch 8/10\n",
"1190/1190 [==============================] - 1s 878us/step - loss: 55295449357.9715\n",
"Epoch 9/10\n",
"1190/1190 [==============================] - 1s 888us/step - loss: 55454267036.0504\n",
"Epoch 10/10\n",
"1190/1190 [==============================] - 1s 869us/step - loss: 56319818830.2401\n",
"510/510 [==============================] - 0s 689us/step\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sK31cMWkfpnj",
"outputId": "605b8e66-8c31-41d0-f73b-92fc5b63907f"
},
"source": [
"mse = get_mse(y_test, prediction)\n",
"mad = get_mad(prediction)\n",
"\n",
"print(\"MSE = %f\" % (mse))\n",
"print(\"MAD = %f\" % (mad))"
],
"execution_count": 214,
"outputs": [
{
"output_type": "stream",
"text": [
"MSE = 57811779353.847527\n",
"MAD = 220.994705\n"
],
"name": "stdout"
}
]
}
]
}
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