From cf1fcab7063d23460f3a2a77bb46fa28bb2db486 Mon Sep 17 00:00:00 2001 From: s452487 Date: Sun, 14 Apr 2024 17:30:10 +0200 Subject: [PATCH] Upload files to "/" --- train.ipynb | 519 +++++++++++++++++++++++++++++++++++++++++++++++++ validate.ipynb | 204 +++++++++++++++++++ 2 files changed, 723 insertions(+) create mode 100644 train.ipynb create mode 100644 validate.ipynb diff --git a/train.ipynb b/train.ipynb new file mode 100644 index 0000000..351c8f5 --- /dev/null +++ b/train.ipynb @@ -0,0 +1,519 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import zipfile\n", + "with zipfile.ZipFile(\"dataset_cleaned.zip\", 'r') as zip_ref:\n", + " zip_ref.extractall(\"dataset_cleaned_extracted\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "import pandas as pd\n", + "# W pobranym zbiorze danych jest kilka podzbiorów więc celowo otwieram ten z NaN, żeby manualnie go oczyścić dla praktyki\n", + "train = pd.read_csv(\"dataset_cleaned_extracted/train.csv\")\n", + "test = pd.read_csv(\"dataset_cleaned_extracted/test.csv\")\n", + "valid = pd.read_csv(\"dataset_cleaned_extracted/valid.csv\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['Male', 'GeneralHealth', 'PhysicalHealthDays', 'MentalHealthDays',\n", + " 'PhysicalActivities', 'SleepHours', 'RemovedTeeth', 'HadHeartAttack',\n", + " 'HadAngina', 'HadStroke', 'HadAsthma', 'HadSkinCancer', 'HadCOPD',\n", + " 'HadDepressiveDisorder', 'HadKidneyDisease', 'HadArthritis',\n", + " 'HadDiabetes', 'DeafOrHardOfHearing', 'BlindOrVisionDifficulty',\n", + " 'DifficultyConcentrating', 'DifficultyWalking',\n", + " 'DifficultyDressingBathing', 'DifficultyErrands', 'SmokerStatus',\n", + " 'ECigaretteUsage', 'ChestScan', 'HeightInMeters', 'WeightInKilograms',\n", + " 'BMI', 'AlcoholDrinkers', 'HIVTesting', 'FluVaxLast12', 'PneumoVaxEver',\n", + " 'TetanusLast10Tdap', 'HighRiskLastYear', 'CovidPos'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "num_columns = train.select_dtypes(['float64']).columns\n", + "print(num_columns)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": "36" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(num_columns)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Male', 'GeneralHealth', 'PhysicalHealthDays', 'MentalHealthDays', 'PhysicalActivities', 'SleepHours', 'RemovedTeeth', 'HadAngina', 'HadStroke', 'HadAsthma', 'HadSkinCancer', 'HadCOPD', 'HadDepressiveDisorder', 'HadKidneyDisease', 'HadArthritis', 'HadDiabetes', 'DeafOrHardOfHearing', 'BlindOrVisionDifficulty', 'DifficultyConcentrating', 'DifficultyWalking', 'DifficultyDressingBathing', 'DifficultyErrands', 'SmokerStatus', 'ECigaretteUsage', 'ChestScan', 'HeightInMeters', 'WeightInKilograms', 'BMI', 'AlcoholDrinkers', 'HIVTesting', 'FluVaxLast12', 'PneumoVaxEver', 'TetanusLast10Tdap', 'HighRiskLastYear', 'CovidPos']\n" + ] + } + ], + "source": [ + "x_columns = ['Male', 'GeneralHealth', 'PhysicalHealthDays', 'MentalHealthDays',\n", + " 'PhysicalActivities', 'SleepHours', 'RemovedTeeth',\n", + " 'HadAngina', 'HadStroke', 'HadAsthma', 'HadSkinCancer', 'HadCOPD',\n", + " 'HadDepressiveDisorder', 'HadKidneyDisease', 'HadArthritis',\n", + " 'HadDiabetes', 'DeafOrHardOfHearing', 'BlindOrVisionDifficulty',\n", + " 'DifficultyConcentrating', 'DifficultyWalking',\n", + " 'DifficultyDressingBathing', 'DifficultyErrands', 'SmokerStatus',\n", + " 'ECigaretteUsage', 'ChestScan', 'HeightInMeters', 'WeightInKilograms',\n", + " 'BMI', 'AlcoholDrinkers', 'HIVTesting', 'FluVaxLast12', 'PneumoVaxEver',\n", + " 'TetanusLast10Tdap', 'HighRiskLastYear', 'CovidPos']\n", + "print(x_columns)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "data": { + "text/plain": "35" + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(x_columns)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [], + "source": [ + "y_column = 'HadHeartAttack'" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [], + "source": [ + "train_x = train[x_columns]\n", + "train_y = train[y_column]\n", + "\n", + "test_x = test[x_columns]\n", + "test_y = test[y_column]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 676617 entries, 0 to 676616\n", + "Data columns (total 41 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Unnamed: 0 676617 non-null int64 \n", + " 1 State 676617 non-null object \n", + " 2 Male 676617 non-null float64\n", + " 3 GeneralHealth 676617 non-null float64\n", + " 4 PhysicalHealthDays 676617 non-null float64\n", + " 5 MentalHealthDays 676617 non-null float64\n", + " 6 LastCheckupTime 676617 non-null object \n", + " 7 PhysicalActivities 676617 non-null float64\n", + " 8 SleepHours 676617 non-null float64\n", + " 9 RemovedTeeth 676617 non-null float64\n", + " 10 HadHeartAttack 676617 non-null float64\n", + " 11 HadAngina 676617 non-null float64\n", + " 12 HadStroke 676617 non-null float64\n", + " 13 HadAsthma 676617 non-null float64\n", + " 14 HadSkinCancer 676617 non-null float64\n", + " 15 HadCOPD 676617 non-null float64\n", + " 16 HadDepressiveDisorder 676617 non-null float64\n", + " 17 HadKidneyDisease 676617 non-null float64\n", + " 18 HadArthritis 676617 non-null float64\n", + " 19 HadDiabetes 676617 non-null float64\n", + " 20 DeafOrHardOfHearing 676617 non-null float64\n", + " 21 BlindOrVisionDifficulty 676617 non-null float64\n", + " 22 DifficultyConcentrating 676617 non-null float64\n", + " 23 DifficultyWalking 676617 non-null float64\n", + " 24 DifficultyDressingBathing 676617 non-null float64\n", + " 25 DifficultyErrands 676617 non-null float64\n", + " 26 SmokerStatus 676617 non-null float64\n", + " 27 ECigaretteUsage 676617 non-null float64\n", + " 28 ChestScan 676617 non-null float64\n", + " 29 RaceEthnicityCategory 676617 non-null object \n", + " 30 AgeCategory 676617 non-null object \n", + " 31 HeightInMeters 676617 non-null float64\n", + " 32 WeightInKilograms 676617 non-null float64\n", + " 33 BMI 676617 non-null float64\n", + " 34 AlcoholDrinkers 676617 non-null float64\n", + " 35 HIVTesting 676617 non-null float64\n", + " 36 FluVaxLast12 676617 non-null float64\n", + " 37 PneumoVaxEver 676617 non-null float64\n", + " 38 TetanusLast10Tdap 676617 non-null float64\n", + " 39 HighRiskLastYear 676617 non-null float64\n", + " 40 CovidPos 676617 non-null float64\n", + "dtypes: float64(36), int64(1), object(4)\n", + "memory usage: 211.6+ MB\n" + ] + } + ], + "source": [ + "train.info()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### Definiowanie modelu" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from keras import layers\n", + "from keras.optimizers import Adam\n", + "def create_model():\n", + " inputs = keras.Input(shape=(35,))\n", + " dense1 = layers.Dense(64, activation=\"relu\")(inputs)\n", + " dropout1 = layers.Dropout(0.2)(dense1)\n", + " dense2 = layers.Dense(32, activation=\"relu\")(dropout1)\n", + " dropout2 = layers.Dropout(0.2)(dense2)\n", + " output = layers.Dense(1, activation=\"sigmoid\")(dropout2)\n", + " model = keras.Model(inputs=inputs, outputs=output)\n", + "\n", + " model.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])\n", + " return model\n", + "\n", + "model = create_model()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model_1\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_2 (InputLayer) [(None, 35)] 0 \n", + " \n", + " dense_3 (Dense) (None, 64) 2304 \n", + " \n", + " dropout_2 (Dropout) (None, 64) 0 \n", + " \n", + " dense_4 (Dense) (None, 32) 2080 \n", + " \n", + " dropout_3 (Dropout) (None, 32) 0 \n", + " \n", + " dense_5 (Dense) (None, 1) 33 \n", + " \n", + "=================================================================\n", + "Total params: 4,417\n", + "Trainable params: 4,417\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### Trenowanie modelu" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [], + "source": [ + "callback = keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', patience=3, restore_best_weights=True)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1000\n", + "21145/21145 [==============================] - 21s 963us/step - loss: 0.4343 - accuracy: 0.7991 - val_loss: 0.3911 - val_accuracy: 0.8412\n", + "Epoch 2/1000\n", + "21145/21145 [==============================] - 20s 957us/step - loss: 0.4262 - accuracy: 0.8043 - val_loss: 0.3980 - val_accuracy: 0.8347\n", + "Epoch 3/1000\n", + "21145/21145 [==============================] - 20s 959us/step - loss: 0.4227 - accuracy: 0.8057 - val_loss: 0.3904 - val_accuracy: 0.8396\n", + "Epoch 4/1000\n", + "21145/21145 [==============================] - 20s 950us/step - loss: 0.4202 - accuracy: 0.8073 - val_loss: 0.4032 - val_accuracy: 0.8285\n", + "Epoch 5/1000\n", + "21145/21145 [==============================] - 20s 962us/step - loss: 0.4184 - accuracy: 0.8083 - val_loss: 0.3639 - val_accuracy: 0.8613\n", + "Epoch 6/1000\n", + "21145/21145 [==============================] - 20s 965us/step - loss: 0.4172 - accuracy: 0.8086 - val_loss: 0.3897 - val_accuracy: 0.8328\n", + "Epoch 7/1000\n", + "21145/21145 [==============================] - 20s 954us/step - loss: 0.4155 - accuracy: 0.8094 - val_loss: 0.4143 - val_accuracy: 0.8272\n", + "Epoch 8/1000\n", + "21145/21145 [==============================] - 21s 970us/step - loss: 0.4145 - accuracy: 0.8102 - val_loss: 0.4026 - val_accuracy: 0.8323\n" + ] + } + ], + "source": [ + "history = model.fit(train_x, train_y, validation_data=(test_x, test_y), epochs=1000, callbacks=[callback])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### Zapisywanie modelu" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [], + "source": [ + "model.save(\"model_v1.keras\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### Testowanie na zbiorze walidacyjnym" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [], + "source": [ + "valid_x = valid[x_columns]\n", + "valid_y = valid[y_column]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 36, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1392/1392 [==============================] - 1s 569us/step\n", + "Poprawność na zbiorze walidacyjnym: 86.15%\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "predictions = model.predict(valid_x)[:,0]\n", + "true_answers = valid_y.to_numpy()\n", + "validation_accuracy = np.sum(np.rint(predictions) == true_answers)/len(true_answers)\n", + "print(f\"Poprawność na zbiorze walidacyjnym: {validation_accuracy:.2%}\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 37, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.08692811 0.12067404 0.31880796 0.64843357 0.15188715 0.06517262\n", + " 0.03407578 0.49311596 0.00781232 0.2089161 0.46056542 0.45341685\n", + " 0.4294767 0.25619727 0.20345858 0.2302334 0.38631877 0.36519188\n", + " 0.04014764 0.23888215 0.27519897 0.08928084 0.05204074 0.42043713\n", + " 0.19055638 0.29787344 0.23068897 0.88435644 0.03139259 0.95048493\n", + " 0.2457671 0.5858893 0.02678488 0.06240147 0.52132165 0.01431455\n", + " 0.02444405 0.07804424 0.11274771 0.12714393 0.35450152 0.01294624\n", + " 0.190797 0.07512036 0.48486376 0.06140704 0.9019506 0.08810509\n", + " 0.61831665 0.15642735 0.03310075 0.04532438 0.10763614 0.4277772\n", + " 0.20325996 0.8980398 0.7491019 0.38502344 0.03970775 0.0401529\n", + " 0.03046079 0.10123587 0.04993626 0.05702 0.18049946 0.1223311\n", + " 0.731555 0.40104443 0.18443953 0.1265702 0.07467585 0.03895461\n", + " 0.35271063 0.38039213 0.4450048 0.03670818 0.05534125 0.91664517\n", + " 0.413391 0.12545326 0.11306539 0.4350903 0.48778924 0.40804324\n", + " 0.33885244 0.21948677 0.01242744 0.02531701 0.6693964 0.15393472\n", + " 0.9307252 0.09181138 0.05571133 0.1261858 0.02687709 0.27069062\n", + " 0.22613294 0.20686075 0.47390068 0.40349996]\n" + ] + } + ], + "source": [ + "print(predictions[:100])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 38, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 1. 0. 1. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.\n", + " 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0.]\n" + ] + } + ], + "source": [ + "print(np.rint(predictions)[:100])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 39, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0.]\n" + ] + } + ], + "source": [ + "print(true_answers[:100])" + ], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/validate.ipynb b/validate.ipynb new file mode 100644 index 0000000..61ca113 --- /dev/null +++ b/validate.ipynb @@ -0,0 +1,204 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import zipfile\n", + "with zipfile.ZipFile(\"dataset_cleaned.zip\", 'r') as zip_ref:\n", + " zip_ref.extractall(\"dataset_cleaned_extracted\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [], + "source": [ + "import pandas as pd\n", + "valid = pd.read_csv(\"dataset_cleaned_extracted/valid.csv\")\n", + "\n", + "x_columns = ['Male', 'GeneralHealth', 'PhysicalHealthDays', 'MentalHealthDays',\n", + " 'PhysicalActivities', 'SleepHours', 'RemovedTeeth',\n", + " 'HadAngina', 'HadStroke', 'HadAsthma', 'HadSkinCancer', 'HadCOPD',\n", + " 'HadDepressiveDisorder', 'HadKidneyDisease', 'HadArthritis',\n", + " 'HadDiabetes', 'DeafOrHardOfHearing', 'BlindOrVisionDifficulty',\n", + " 'DifficultyConcentrating', 'DifficultyWalking',\n", + " 'DifficultyDressingBathing', 'DifficultyErrands', 'SmokerStatus',\n", + " 'ECigaretteUsage', 'ChestScan', 'HeightInMeters', 'WeightInKilograms',\n", + " 'BMI', 'AlcoholDrinkers', 'HIVTesting', 'FluVaxLast12', 'PneumoVaxEver',\n", + " 'TetanusLast10Tdap', 'HighRiskLastYear', 'CovidPos']\n", + "y_column = 'HadHeartAttack'\n", + "\n", + "valid_x = valid[x_columns]\n", + "valid_y = valid[y_column]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "from tensorflow import keras\n", + "model = keras.models.load_model('model_v1.keras')" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1392/1392 [==============================] - 1s 566us/step\n", + "Poprawność na zbiorze walidacyjnym: 86.15%\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "predictions = model.predict(valid_x)[:,0]\n", + "true_answers = valid_y.to_numpy()\n", + "validation_accuracy = np.sum(np.rint(predictions) == true_answers)/len(true_answers)\n", + "print(f\"Poprawność na zbiorze walidacyjnym: {validation_accuracy:.2%}\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.08692811 0.12067404 0.31880796 0.64843357 0.15188715 0.06517262\n", + " 0.03407578 0.49311596 0.00781232 0.2089161 0.46056542 0.45341685\n", + " 0.4294767 0.25619727 0.20345858 0.2302334 0.38631877 0.36519188\n", + " 0.04014764 0.23888215 0.27519897 0.08928084 0.05204074 0.42043713\n", + " 0.19055638 0.29787344 0.23068897 0.88435644 0.03139259 0.95048493\n", + " 0.2457671 0.5858893 0.02678488 0.06240147 0.52132165 0.01431455\n", + " 0.02444405 0.07804424 0.11274771 0.12714393 0.35450152 0.01294624\n", + " 0.190797 0.07512036 0.48486376 0.06140704 0.9019506 0.08810509\n", + " 0.61831665 0.15642735 0.03310075 0.04532438 0.10763614 0.4277772\n", + " 0.20325996 0.8980398 0.7491019 0.38502344 0.03970775 0.0401529\n", + " 0.03046079 0.10123587 0.04993626 0.05702 0.18049946 0.1223311\n", + " 0.731555 0.40104443 0.18443953 0.1265702 0.07467585 0.03895461\n", + " 0.35271063 0.38039213 0.4450048 0.03670818 0.05534125 0.91664517\n", + " 0.413391 0.12545326 0.11306539 0.4350903 0.48778924 0.40804324\n", + " 0.33885244 0.21948677 0.01242744 0.02531701 0.6693964 0.15393472\n", + " 0.9307252 0.09181138 0.05571133 0.1261858 0.02687709 0.27069062\n", + " 0.22613294 0.20686075 0.47390068 0.40349996]\n" + ] + } + ], + "source": [ + "print(predictions[:100])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 1. 0. 1. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.\n", + " 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0.]\n" + ] + } + ], + "source": [ + "print(np.rint(predictions)[:100])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0.]\n" + ] + } + ], + "source": [ + "print(true_answers[:100])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "np.savetxt(\"predictions.txt\",predictions)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [], + "source": [ + "np.savetxt(\"predictions_two_digits.txt\",predictions, fmt='%1.2f')" + ], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}