{
"cells": [
{
"metadata": {
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"trusted": true
},
"cell_type": "code",
"source": "# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.",
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": "['creditcard.csv']\n",
"name": "stdout"
}
]
},
{
"metadata": {
"_uuid": "04bcbe2c5f6b3f74f6f8732c41e8e8e15654d311",
"_cell_guid": "1fa9c421-e3e1-4a2c-978f-28f78e01ab34",
"trusted": true
},
"cell_type": "code",
"source": "from numpy.random import seed\nseed(1)\nfrom tensorflow import set_random_seed\nset_random_seed(2)",
"execution_count": 2,
"outputs": []
},
{
"metadata": {
"_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
"_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
"trusted": true
},
"cell_type": "code",
"source": "df = pd.read_csv('../input/creditcard.csv')",
"execution_count": 3,
"outputs": []
},
{
"metadata": {
"_uuid": "24bc772bdd624e7d844b74020e273ffe3def4246",
"_cell_guid": "ad3d861f-12e4-49ca-a221-6d8bd6be6f8a",
"trusted": true
},
"cell_type": "code",
"source": "df.head()",
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 4,
"data": {
"text/plain": " Time V1 V2 V3 ... V27 V28 Amount Class\n0 0.0 -1.359807 -0.072781 2.536347 ... 0.133558 -0.021053 149.62 0\n1 0.0 1.191857 0.266151 0.166480 ... -0.008983 0.014724 2.69 0\n2 1.0 -1.358354 -1.340163 1.773209 ... -0.055353 -0.059752 378.66 0\n3 1.0 -0.966272 -0.185226 1.792993 ... 0.062723 0.061458 123.50 0\n4 2.0 -1.158233 0.877737 1.548718 ... 0.219422 0.215153 69.99 0\n\n[5 rows x 31 columns]",
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\n\n
\n \n \n | \n Time | \n V1 | \n V2 | \n V3 | \n V4 | \n V5 | \n V6 | \n V7 | \n V8 | \n V9 | \n V10 | \n V11 | \n V12 | \n V13 | \n V14 | \n V15 | \n V16 | \n V17 | \n V18 | \n V19 | \n V20 | \n V21 | \n V22 | \n V23 | \n V24 | \n V25 | \n V26 | \n V27 | \n V28 | \n Amount | \n Class | \n
\n \n \n \n 0 | \n 0.0 | \n -1.359807 | \n -0.072781 | \n 2.536347 | \n 1.378155 | \n -0.338321 | \n 0.462388 | \n 0.239599 | \n 0.098698 | \n 0.363787 | \n 0.090794 | \n -0.551600 | \n -0.617801 | \n -0.991390 | \n -0.311169 | \n 1.468177 | \n -0.470401 | \n 0.207971 | \n 0.025791 | \n 0.403993 | \n 0.251412 | \n -0.018307 | \n 0.277838 | \n -0.110474 | \n 0.066928 | \n 0.128539 | \n -0.189115 | \n 0.133558 | \n -0.021053 | \n 149.62 | \n 0 | \n
\n \n 1 | \n 0.0 | \n 1.191857 | \n 0.266151 | \n 0.166480 | \n 0.448154 | \n 0.060018 | \n -0.082361 | \n -0.078803 | \n 0.085102 | \n -0.255425 | \n -0.166974 | \n 1.612727 | \n 1.065235 | \n 0.489095 | \n -0.143772 | \n 0.635558 | \n 0.463917 | \n -0.114805 | \n -0.183361 | \n -0.145783 | \n -0.069083 | \n -0.225775 | \n -0.638672 | \n 0.101288 | \n -0.339846 | \n 0.167170 | \n 0.125895 | \n -0.008983 | \n 0.014724 | \n 2.69 | \n 0 | \n
\n \n 2 | \n 1.0 | \n -1.358354 | \n -1.340163 | \n 1.773209 | \n 0.379780 | \n -0.503198 | \n 1.800499 | \n 0.791461 | \n 0.247676 | \n -1.514654 | \n 0.207643 | \n 0.624501 | \n 0.066084 | \n 0.717293 | \n -0.165946 | \n 2.345865 | \n -2.890083 | \n 1.109969 | \n -0.121359 | \n -2.261857 | \n 0.524980 | \n 0.247998 | \n 0.771679 | \n 0.909412 | \n -0.689281 | \n -0.327642 | \n -0.139097 | \n -0.055353 | \n -0.059752 | \n 378.66 | \n 0 | \n
\n \n 3 | \n 1.0 | \n -0.966272 | \n -0.185226 | \n 1.792993 | \n -0.863291 | \n -0.010309 | \n 1.247203 | \n 0.237609 | \n 0.377436 | \n -1.387024 | \n -0.054952 | \n -0.226487 | \n 0.178228 | \n 0.507757 | \n -0.287924 | \n -0.631418 | \n -1.059647 | \n -0.684093 | \n 1.965775 | \n -1.232622 | \n -0.208038 | \n -0.108300 | \n 0.005274 | \n -0.190321 | \n -1.175575 | \n 0.647376 | \n -0.221929 | \n 0.062723 | \n 0.061458 | \n 123.50 | \n 0 | \n
\n \n 4 | \n 2.0 | \n -1.158233 | \n 0.877737 | \n 1.548718 | \n 0.403034 | \n -0.407193 | \n 0.095921 | \n 0.592941 | \n -0.270533 | \n 0.817739 | \n 0.753074 | \n -0.822843 | \n 0.538196 | \n 1.345852 | \n -1.119670 | \n 0.175121 | \n -0.451449 | \n -0.237033 | \n -0.038195 | \n 0.803487 | \n 0.408542 | \n -0.009431 | \n 0.798278 | \n -0.137458 | \n 0.141267 | \n -0.206010 | \n 0.502292 | \n 0.219422 | \n 0.215153 | \n 69.99 | \n 0 | \n
\n \n
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"metadata": {}
}
]
},
{
"metadata": {
"_uuid": "7f9f454f8341834b0c6ab1a3de99b2577dcd7dd0",
"_cell_guid": "ac5f5f90-1607-4c5e-b0e7-845c724c7521",
"trusted": true
},
"cell_type": "code",
"source": "df.describe()",
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 5,
"data": {
"text/plain": " Time V1 ... Amount Class\ncount 284807.000000 2.848070e+05 ... 284807.000000 284807.000000\nmean 94813.859575 3.919560e-15 ... 88.349619 0.001727\nstd 47488.145955 1.958696e+00 ... 250.120109 0.041527\nmin 0.000000 -5.640751e+01 ... 0.000000 0.000000\n25% 54201.500000 -9.203734e-01 ... 5.600000 0.000000\n50% 84692.000000 1.810880e-02 ... 22.000000 0.000000\n75% 139320.500000 1.315642e+00 ... 77.165000 0.000000\nmax 172792.000000 2.454930e+00 ... 25691.160000 1.000000\n\n[8 rows x 31 columns]",
"text/html": "\n\n
\n \n \n | \n Time | \n V1 | \n V2 | \n V3 | \n V4 | \n V5 | \n V6 | \n V7 | \n V8 | \n V9 | \n V10 | \n V11 | \n V12 | \n V13 | \n V14 | \n V15 | \n V16 | \n V17 | \n V18 | \n V19 | \n V20 | \n V21 | \n V22 | \n V23 | \n V24 | \n V25 | \n V26 | \n V27 | \n V28 | \n Amount | \n Class | \n
\n \n \n \n count | \n 284807.000000 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 2.848070e+05 | \n 284807.000000 | \n 284807.000000 | \n
\n \n mean | \n 94813.859575 | \n 3.919560e-15 | \n 5.688174e-16 | \n -8.769071e-15 | \n 2.782312e-15 | \n -1.552563e-15 | \n 2.010663e-15 | \n -1.694249e-15 | \n -1.927028e-16 | \n -3.137024e-15 | \n 1.768627e-15 | \n 9.170318e-16 | \n -1.810658e-15 | \n 1.693438e-15 | \n 1.479045e-15 | \n 3.482336e-15 | \n 1.392007e-15 | \n -7.528491e-16 | \n 4.328772e-16 | \n 9.049732e-16 | \n 5.085503e-16 | \n 1.537294e-16 | \n 7.959909e-16 | \n 5.367590e-16 | \n 4.458112e-15 | \n 1.453003e-15 | \n 1.699104e-15 | \n -3.660161e-16 | \n -1.206049e-16 | \n 88.349619 | \n 0.001727 | \n
\n \n std | \n 47488.145955 | \n 1.958696e+00 | \n 1.651309e+00 | \n 1.516255e+00 | \n 1.415869e+00 | \n 1.380247e+00 | \n 1.332271e+00 | \n 1.237094e+00 | \n 1.194353e+00 | \n 1.098632e+00 | \n 1.088850e+00 | \n 1.020713e+00 | \n 9.992014e-01 | \n 9.952742e-01 | \n 9.585956e-01 | \n 9.153160e-01 | \n 8.762529e-01 | \n 8.493371e-01 | \n 8.381762e-01 | \n 8.140405e-01 | \n 7.709250e-01 | \n 7.345240e-01 | \n 7.257016e-01 | \n 6.244603e-01 | \n 6.056471e-01 | \n 5.212781e-01 | \n 4.822270e-01 | \n 4.036325e-01 | \n 3.300833e-01 | \n 250.120109 | \n 0.041527 | \n
\n \n min | \n 0.000000 | \n -5.640751e+01 | \n -7.271573e+01 | \n -4.832559e+01 | \n -5.683171e+00 | \n -1.137433e+02 | \n -2.616051e+01 | \n -4.355724e+01 | \n -7.321672e+01 | \n -1.343407e+01 | \n -2.458826e+01 | \n -4.797473e+00 | \n -1.868371e+01 | \n -5.791881e+00 | \n -1.921433e+01 | \n -4.498945e+00 | \n -1.412985e+01 | \n -2.516280e+01 | \n -9.498746e+00 | \n -7.213527e+00 | \n -5.449772e+01 | \n -3.483038e+01 | \n -1.093314e+01 | \n -4.480774e+01 | \n -2.836627e+00 | \n -1.029540e+01 | \n -2.604551e+00 | \n -2.256568e+01 | \n -1.543008e+01 | \n 0.000000 | \n 0.000000 | \n
\n \n 25% | \n 54201.500000 | \n -9.203734e-01 | \n -5.985499e-01 | \n -8.903648e-01 | \n -8.486401e-01 | \n -6.915971e-01 | \n -7.682956e-01 | \n -5.540759e-01 | \n -2.086297e-01 | \n -6.430976e-01 | \n -5.354257e-01 | \n -7.624942e-01 | \n -4.055715e-01 | \n -6.485393e-01 | \n -4.255740e-01 | \n -5.828843e-01 | \n -4.680368e-01 | \n -4.837483e-01 | \n -4.988498e-01 | \n -4.562989e-01 | \n -2.117214e-01 | \n -2.283949e-01 | \n -5.423504e-01 | \n -1.618463e-01 | \n -3.545861e-01 | \n -3.171451e-01 | \n -3.269839e-01 | \n -7.083953e-02 | \n -5.295979e-02 | \n 5.600000 | \n 0.000000 | \n
\n \n 50% | \n 84692.000000 | \n 1.810880e-02 | \n 6.548556e-02 | \n 1.798463e-01 | \n -1.984653e-02 | \n -5.433583e-02 | \n -2.741871e-01 | \n 4.010308e-02 | \n 2.235804e-02 | \n -5.142873e-02 | \n -9.291738e-02 | \n -3.275735e-02 | \n 1.400326e-01 | \n -1.356806e-02 | \n 5.060132e-02 | \n 4.807155e-02 | \n 6.641332e-02 | \n -6.567575e-02 | \n -3.636312e-03 | \n 3.734823e-03 | \n -6.248109e-02 | \n -2.945017e-02 | \n 6.781943e-03 | \n -1.119293e-02 | \n 4.097606e-02 | \n 1.659350e-02 | \n -5.213911e-02 | \n 1.342146e-03 | \n 1.124383e-02 | \n 22.000000 | \n 0.000000 | \n
\n \n 75% | \n 139320.500000 | \n 1.315642e+00 | \n 8.037239e-01 | \n 1.027196e+00 | \n 7.433413e-01 | \n 6.119264e-01 | \n 3.985649e-01 | \n 5.704361e-01 | \n 3.273459e-01 | \n 5.971390e-01 | \n 4.539234e-01 | \n 7.395934e-01 | \n 6.182380e-01 | \n 6.625050e-01 | \n 4.931498e-01 | \n 6.488208e-01 | \n 5.232963e-01 | \n 3.996750e-01 | \n 5.008067e-01 | \n 4.589494e-01 | \n 1.330408e-01 | \n 1.863772e-01 | \n 5.285536e-01 | \n 1.476421e-01 | \n 4.395266e-01 | \n 3.507156e-01 | \n 2.409522e-01 | \n 9.104512e-02 | \n 7.827995e-02 | \n 77.165000 | \n 0.000000 | \n
\n \n max | \n 172792.000000 | \n 2.454930e+00 | \n 2.205773e+01 | \n 9.382558e+00 | \n 1.687534e+01 | \n 3.480167e+01 | \n 7.330163e+01 | \n 1.205895e+02 | \n 2.000721e+01 | \n 1.559499e+01 | \n 2.374514e+01 | \n 1.201891e+01 | \n 7.848392e+00 | \n 7.126883e+00 | \n 1.052677e+01 | \n 8.877742e+00 | \n 1.731511e+01 | \n 9.253526e+00 | \n 5.041069e+00 | \n 5.591971e+00 | \n 3.942090e+01 | \n 2.720284e+01 | \n 1.050309e+01 | \n 2.252841e+01 | \n 4.584549e+00 | \n 7.519589e+00 | \n 3.517346e+00 | \n 3.161220e+01 | \n 3.384781e+01 | \n 25691.160000 | \n 1.000000 | \n
\n \n
\n
"
},
"metadata": {}
}
]
},
{
"metadata": {
"_uuid": "ae27e9aac252b8abebe73a7f152e8285f6ae6671",
"_cell_guid": "8f0c6894-0f3c-4f1a-9b2e-5155cc434ab8",
"trusted": true
},
"cell_type": "code",
"source": "df.isnull().sum()",
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 6,
"data": {
"text/plain": "Time 0\nV1 0\nV2 0\nV3 0\nV4 0\nV5 0\nV6 0\nV7 0\nV8 0\nV9 0\nV10 0\nV11 0\nV12 0\nV13 0\nV14 0\nV15 0\nV16 0\nV17 0\nV18 0\nV19 0\nV20 0\nV21 0\nV22 0\nV23 0\nV24 0\nV25 0\nV26 0\nV27 0\nV28 0\nAmount 0\nClass 0\ndtype: int64"
},
"metadata": {}
}
]
},
{
"metadata": {
"_uuid": "7ba46b55bc860137cbde1b97572a918dec5970d3",
"_cell_guid": "40293e6e-ec35-4847-90f1-1e6e89f70b91",
"trusted": true
},
"cell_type": "code",
"source": "df = df.drop('Time',axis=1)",
"execution_count": 7,
"outputs": []
},
{
"metadata": {
"_uuid": "8ab18d55eacc48d3f578c2c1977a789f9b95e03e",
"_cell_guid": "25ea497f-93a3-4818-b1e5-a40c9ae9b81d",
"trusted": true
},
"cell_type": "code",
"source": "X = df.drop('Class',axis=1).values \ny = df['Class'].values",
"execution_count": 8,
"outputs": []
},
{
"metadata": {
"_uuid": "4e153e0a8bbd54231af019089db7ceac7d4a3ab2",
"_cell_guid": "c4737b0e-f780-45b4-b1cc-c0bd1d419b41",
"trusted": true
},
"cell_type": "code",
"source": "X.shape",
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 9,
"data": {
"text/plain": "(284807, 29)"
},
"metadata": {}
}
]
},
{
"metadata": {
"_uuid": "66ce9da4edfea3e8b6619d5f543b365899a59a5e",
"_cell_guid": "5788dbeb-8aa2-42a5-99af-b4e367de3808",
"trusted": true
},
"cell_type": "code",
"source": "X -= X.min(axis=0)\nX /= X.max(axis=0)",
"execution_count": 10,
"outputs": []
},
{
"metadata": {
"_uuid": "c36820c67500d54458d9b22ebc2293f2e8ccf99f",
"_cell_guid": "57b2f8a6-9c45-4f60-a0ff-8aef07b2f484",
"trusted": true
},
"cell_type": "code",
"source": "X.mean()",
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 11,
"data": {
"text/plain": "0.5213456986251124"
},
"metadata": {}
}
]
},
{
"metadata": {
"_uuid": "91d77fc484400c0bc3ba4c3b16ebd9873d3da966",
"_cell_guid": "f79cfb3e-0a02-4052-b057-dfd6b96ac026",
"trusted": true
},
"cell_type": "code",
"source": "X.shape",
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 12,
"data": {
"text/plain": "(284807, 29)"
},
"metadata": {}
}
]
},
{
"metadata": {
"_uuid": "156872c244cdf82a28daa404fe1ebaaa96c52d0d",
"_cell_guid": "78e7cc64-e345-45c4-8c4e-52aa50cb9c21",
"trusted": true
},
"cell_type": "code",
"source": "from sklearn.model_selection import train_test_split\nX_train, X_test, y_train,y_test = train_test_split(X,y,test_size=0.1)",
"execution_count": 13,
"outputs": []
},
{
"metadata": {
"_uuid": "958d9f0b9143a37842c6510696173e357817397d",
"_cell_guid": "26067623-40ff-44d1-9fbf-1736f9d5a967",
"trusted": true
},
"cell_type": "code",
"source": "from keras.models import Model\nfrom keras.layers import Input, Dense",
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"text": "Using TensorFlow backend.\n",
"name": "stderr"
}
]
},
{
"metadata": {
"_uuid": "0e6386095559dafe1c3f4660149894591ea6d0ff",
"_cell_guid": "cf479c9f-e2e6-43c2-be27-852b70f2796f",
"trusted": true
},
"cell_type": "code",
"source": "data_in = Input(shape=(29,))\nencoded = Dense(12,activation='tanh')(data_in)\ndecoded = Dense(29,activation='sigmoid')(encoded)\nautoencoder = Model(data_in,decoded)",
"execution_count": 15,
"outputs": []
},
{
"metadata": {
"_uuid": "b350c49f16744ceff5a0545b44915ca4a85cbfae",
"_cell_guid": "92225494-1294-479f-8003-fb5db2c6db2d",
"trusted": true
},
"cell_type": "code",
"source": "autoencoder.compile(optimizer='adam',loss='mean_squared_error')",
"execution_count": 16,
"outputs": []
},
{
"metadata": {
"_uuid": "9cff9216e21c05d9f7fc2e05a426ed065deadabf",
"_cell_guid": "a4a83280-5afe-413f-89fa-86d67e3a3adb",
"trusted": true
},
"cell_type": "code",
"source": "autoencoder.fit(X_train,\n X_train,\n epochs = 20, \n batch_size=128, \n validation_data=(X_test,X_test))",
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"text": "Train on 256326 samples, validate on 28481 samples\nEpoch 1/20\n256326/256326 [==============================] - 8s 29us/step - loss: 0.0027 - val_loss: 0.0015\nEpoch 2/20\n256326/256326 [==============================] - 7s 26us/step - loss: 0.0012 - val_loss: 7.5711e-04\nEpoch 3/20\n256326/256326 [==============================] - 7s 26us/step - loss: 5.9954e-04 - val_loss: 5.0860e-04\nEpoch 4/20\n256326/256326 [==============================] - 7s 26us/step - loss: 4.3634e-04 - val_loss: 3.8141e-04\nEpoch 5/20\n256326/256326 [==============================] - 7s 26us/step - loss: 3.4394e-04 - val_loss: 3.1408e-04\nEpoch 6/20\n256326/256326 [==============================] - 7s 26us/step - loss: 2.9318e-04 - val_loss: 2.8245e-04\nEpoch 7/20\n256326/256326 [==============================] - 7s 26us/step - loss: 2.6792e-04 - val_loss: 2.5355e-04\nEpoch 8/20\n256326/256326 [==============================] - 7s 26us/step - loss: 2.3752e-04 - val_loss: 2.2938e-04\nEpoch 9/20\n256326/256326 [==============================] - 7s 26us/step - loss: 2.2411e-04 - val_loss: 2.2209e-04\nEpoch 10/20\n256326/256326 [==============================] - 7s 26us/step - loss: 2.1863e-04 - val_loss: 2.1798e-04\nEpoch 11/20\n256326/256326 [==============================] - 7s 26us/step - loss: 2.1538e-04 - val_loss: 2.1600e-04\nEpoch 12/20\n256326/256326 [==============================] - 7s 26us/step - loss: 2.1341e-04 - val_loss: 2.1404e-04\nEpoch 13/20\n256326/256326 [==============================] - 7s 27us/step - loss: 2.1226e-04 - val_loss: 2.1334e-04\nEpoch 14/20\n256326/256326 [==============================] - 7s 27us/step - loss: 2.1146e-04 - val_loss: 2.1264e-04\nEpoch 15/20\n256326/256326 [==============================] - 7s 26us/step - loss: 2.1092e-04 - val_loss: 2.1200e-04\nEpoch 16/20\n256326/256326 [==============================] - 7s 26us/step - loss: 2.1046e-04 - val_loss: 2.1197e-04\nEpoch 17/20\n256326/256326 [==============================] - 7s 26us/step - loss: 2.1005e-04 - val_loss: 2.1131e-04\nEpoch 18/20\n256326/256326 [==============================] - 7s 26us/step - loss: 2.0971e-04 - val_loss: 2.1132e-04\nEpoch 19/20\n256326/256326 [==============================] - 7s 26us/step - loss: 2.0945e-04 - val_loss: 2.1161e-04\nEpoch 20/20\n256326/256326 [==============================] - 7s 26us/step - loss: 2.0920e-04 - val_loss: 2.1059e-04\n",
"name": "stdout"
},
{
"output_type": "execute_result",
"execution_count": 17,
"data": {
"text/plain": ""
},
"metadata": {}
}
]
},
{
"metadata": {
"_uuid": "44a06be6edd99f083d7107919ca2e84f06f661a7",
"_cell_guid": "1496244f-fa4c-430a-924b-3414ef1a015d",
"trusted": true
},
"cell_type": "code",
"source": "X_test.mean()",
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 18,
"data": {
"text/plain": "0.5214011426360144"
},
"metadata": {}
}
]
},
{
"metadata": {
"_uuid": "47e3a4a5ef8a3def84ada50a8db94112f7e0f746",
"_cell_guid": "67f0ac0d-50ab-4e72-b594-f73e7f978fbb",
"trusted": true
},
"cell_type": "code",
"source": "pred = autoencoder.predict(X_test[0:10])",
"execution_count": 19,
"outputs": []
},
{
"metadata": {
"_uuid": "75b98afa5e92031738415f3650f32e0f91a5a4b8",
"_cell_guid": "76d93c84-1419-4a17-b8ce-130c1faa049b",
"trusted": true
},
"cell_type": "code",
"source": "import matplotlib.pyplot as plt\nimport numpy as np\n\nwidth = 0.8\n\nprediction = pred[9]\ntrue_value = X_test[9]\n\nindices = np.arange(len(prediction))\n\nfig = plt.figure(figsize=(10,7))\n\nplt.bar(indices, prediction, width=width, \n color='b', label='Predicted Value')\n\nplt.bar([i+0.25*width for i in indices], true_value, \n width=0.5*width, color='r', alpha=0.5, label='True Value')\n\nplt.xticks(indices+width/2., \n ['V{}'.format(i) for i in range(len(prediction))] )\n\nplt.legend()\n\nplt.show()",
"execution_count": 20,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "