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Copy pathANN classification On intrusion network attacks
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ANN classification On intrusion network attacks
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{
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"metadata": {
"colab": {
"name": "Untitled0.ipynb",
"provenance": [],
"mount_file_id": "1u5v0gP8yCxNQYU_5rKU-HfeuQiCw_cMZ",
"authorship_tag": "ABX9TyMKExrotszWU1AAOqEdz+HA",
"include_colab_link": true
},
"kernelspec": {
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"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/umanniyaz/COVID-19/blob/master/ANN%20classification%20On%20intrusion%20network%20attacks%20\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "0LjwxlXhBVwR",
"colab_type": "code",
"colab": {
"resources": {
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"data": 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",
"ok": true,
"headers": [
[
"content-type",
"application/javascript"
]
],
"status": 200,
"status_text": ""
}
},
"base_uri": "https://localhost:8080/",
"height": 91
},
"outputId": "31a58040-025e-4d12-8ec0-33651510d9fd"
},
"source": [
"from google.colab import files\n",
"\n",
"uploaded = files.upload()\n",
"\n",
"for fn in uploaded.keys():\n",
" print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
" name=fn, length=len(uploaded[fn])))"
],
"execution_count": 1,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <input type=\"file\" id=\"files-cee7d660-15db-486e-b02f-8f496e31c602\" name=\"files[]\" multiple disabled />\n",
" <output id=\"result-cee7d660-15db-486e-b02f-8f496e31c602\">\n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" </output>\n",
" <script src=\"/nbextensions/google.colab/files.js\"></script> "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Saving kddcup_data_10_percent_UNIQUE_Nor_Att_final_6_Attr.csv to kddcup_data_10_percent_UNIQUE_Nor_Att_final_6_Attr (1).csv\n",
"User uploaded file \"kddcup_data_10_percent_UNIQUE_Nor_Att_final_6_Attr.csv\" with length 4232915 bytes\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "EOd2pGRbDifb",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "37e3f848-5e15-4c97-94cc-4f0072f9f2a5"
},
"source": [
"import pandas as pd\n",
"import io\n",
"data=pd.read_csv(io.StringIO(uploaded['kddcup_data_10_percent_UNIQUE_Nor_Att_final_6_Attr.csv'].decode('unicode_escape')))\n",
"data.tail()"
],
"execution_count": 11,
"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>service</th>\n",
" <th>src-bytes</th>\n",
" <th>dst-bytes</th>\n",
" <th>hot</th>\n",
" <th>num-compromised</th>\n",
" <th>same-srv-rate</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>145581</th>\n",
" <td>http</td>\n",
" <td>310</td>\n",
" <td>1881</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>normal.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145582</th>\n",
" <td>http</td>\n",
" <td>282</td>\n",
" <td>2286</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>normal.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145583</th>\n",
" <td>http</td>\n",
" <td>203</td>\n",
" <td>1200</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>normal.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145584</th>\n",
" <td>http</td>\n",
" <td>291</td>\n",
" <td>1200</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>normal.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145585</th>\n",
" <td>http</td>\n",
" <td>219</td>\n",
" <td>1234</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>normal.</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" service src-bytes ... same-srv-rate label\n",
"145581 http 310 ... 1.0 normal.\n",
"145582 http 282 ... 1.0 normal.\n",
"145583 http 203 ... 1.0 normal.\n",
"145584 http 291 ... 1.0 normal.\n",
"145585 http 219 ... 1.0 normal.\n",
"\n",
"[5 rows x 7 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "b5ErsawcBYy-",
"colab_type": "code",
"colab": {}
},
"source": [
"X=data.iloc[:,:].values"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Ja69ta1EMLnB",
"colab_type": "code",
"colab": {}
},
"source": [
"from sklearn.preprocessing import LabelEncoder\n",
"labelencoder_X=LabelEncoder()\n",
"X[:,0]=labelencoder_X.fit_transform(X[:,0])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "wmC4EJOQM1cL",
"colab_type": "code",
"colab": {}
},
"source": [
"df=pd.DataFrame(X)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "X-yw2U1LeBo7",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "b8cc65df-d89b-4149-96ff-19546acba039"
},
"source": [
"df.head()"
],
"execution_count": 67,
"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>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>22</td>\n",
" <td>164</td>\n",
" <td>5450</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>normal.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>22</td>\n",
" <td>222</td>\n",
" <td>486</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>normal.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>22</td>\n",
" <td>218</td>\n",
" <td>1337</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>normal.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>22</td>\n",
" <td>202</td>\n",
" <td>1337</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>normal.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>22</td>\n",
" <td>200</td>\n",
" <td>2032</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>normal.</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6\n",
"0 22 164 5450 0 0 1 normal.\n",
"1 22 222 486 0 0 1 normal.\n",
"2 22 218 1337 0 0 1 normal.\n",
"3 22 202 1337 0 0 1 normal.\n",
"4 22 200 2032 0 0 1 normal."
]
},
"metadata": {
"tags": []
},
"execution_count": 67
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "z1wpzx-ZePBh",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 173
},
"outputId": "27180927-85ef-4b63-b0bb-98b20ef7a838"
},
"source": [
"df.describe()"
],
"execution_count": 80,
"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>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>145586</td>\n",
" <td>145586</td>\n",
" <td>145586</td>\n",
" <td>145586</td>\n",
" <td>145586</td>\n",
" <td>145586.0</td>\n",
" <td>145586</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
" <td>66</td>\n",
" <td>3300</td>\n",
" <td>10725</td>\n",
" <td>22</td>\n",
" <td>23</td>\n",
" <td>99.0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>normal.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>62054</td>\n",
" <td>57872</td>\n",
" <td>67663</td>\n",
" <td>143629</td>\n",
" <td>144596</td>\n",
" <td>90096.0</td>\n",
" <td>87832</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6\n",
"count 145586 145586 145586 145586 145586 145586.0 145586\n",
"unique 66 3300 10725 22 23 99.0 2\n",
"top 22 0 0 0 0 1.0 normal.\n",
"freq 62054 57872 67663 143629 144596 90096.0 87832"
]
},
"metadata": {
"tags": []
},
"execution_count": 80
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "AY0-wef-eQO-",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 313
},
"outputId": "cba8648a-ae7f-48da-df01-442f02553ddb"
},
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"ax=sns.countplot(data['label'], label = 'Count')\n",
"N,A=data['label'].value_counts()\n",
"print('normal', N)\n",
"print('attack', A)"
],
"execution_count": 81,
"outputs": [
{
"output_type": "stream",
"text": [
"normal 87832\n",
"attack 57754\n"
],
"name": "stdout"
},
{
"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": "nWaJxr1Vfxai",
"colab_type": "code",
"colab": {}
},
"source": [
"X=df.iloc[:, 0:-1].values\n",
"y=df.iloc[:, 6].values"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "PN6-iu0tf09j",
"colab_type": "code",
"colab": {}
},
"source": [
"from sklearn.preprocessing import LabelEncoder\n",
"labelencoder_X=LabelEncoder()\n",
"y=labelencoder_X.fit_transform(y)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "-xDAjT1VrMGH",
"colab_type": "code",
"colab": {}
},
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test =train_test_split(X,y, test_size = 0.10, random_state = 0)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "jFwGDzBRrOQ7",
"colab_type": "code",
"colab": {}
},
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"sc=StandardScaler()\n",
"X_train=sc.fit_transform(X_train)\n",
"X_test=sc.fit_transform(X_test)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "IeFWyd5nsWKt",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 238
},
"outputId": "c111d963-d3ee-4063-cb6d-c2c2985fe251"
},
"source": [
"X_train"
],
"execution_count": 117,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[-1.62330129, -0.43633831, -0.04584674, -0.07000389, -0.00809932,\n",
" 0.7716935 ],\n",
" [ 0.98474685, -0.49193043, -0.04743194, -0.07000389, -0.00809932,\n",
" -0.57120252],\n",
" [-0.77952101, -0.03236889, -0.03042407, -0.07000389, -0.00809932,\n",
" 0.7716935 ],\n",
" ...,\n",
" [ 0.98474685, -0.49193043, -0.04743194, -0.07000389, -0.00809932,\n",
" -1.39932174],\n",
" [ 0.98474685, -0.49193043, -0.04743194, -0.07000389, -0.00809932,\n",
" -1.28741373],\n",
" [ 0.98474685, -0.49193043, -0.04743194, -0.07000389, -0.00809932,\n",
" -1.37694013]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 117
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7HW_ME4Us0Vv",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 238
},
"outputId": "7f20c6c1-b8ee-43ba-b305-4c156aadfc6b"
},
"source": [
"X_test"
],
"execution_count": 118,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[ 0.99125201, -0.4930218 , -0.04357149, -0.07204251, -0.06718572,\n",
" -1.35425367],\n",
" [-0.77059087, 0.07733494, -0.037128 , -0.07204251, -0.06718572,\n",
" 0.77210948],\n",
" [-0.77059087, -0.04673617, -0.03904835, -0.07204251, -0.06718572,\n",
" 0.77210948],\n",
" ...,\n",
" [ 0.99125201, -0.4930218 , -0.04357149, -0.07204251, -0.06718572,\n",
" -1.35425367],\n",
" [ 0.99125201, -0.4930218 , -0.04357149, -0.07204251, -0.06718572,\n",
" -1.35425367],\n",
" [ 0.99125201, -0.4930218 , -0.04357149, -0.07204251, -0.06718572,\n",
" -1.26472259]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 118
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "a-xDivHus2mm",
"colab_type": "code",
"colab": {}
},
"source": [
"import keras\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense\n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "hPJrTz37tKUq",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 139
},
"outputId": "a91327e6-9854-40cc-c673-6e1f9bcb2a0e"
},
"source": [
"classifier = Sequential()\n",
"classifier.add(Dense(output_dim=3, init='uniform', activation = 'relu' , input_dim=6))\n",
"\n",
"\n",
"classifier.add(Dense(output_dim=3, init='uniform', activation = 'relu'))\n",
"\n",
"\n",
"classifier.add(Dense(output_dim=1, init='uniform', activation = 'sigmoid'))"
],
"execution_count": 120,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", input_dim=6, units=3, kernel_initializer=\"uniform\")`\n",
" \n",
"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=3, kernel_initializer=\"uniform\")`\n",
" \"\"\"\n",
"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"sigmoid\", units=1, kernel_initializer=\"uniform\")`\n",
" \n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "8C8uFR8ctvbP",
"colab_type": "code",
"colab": {}
},
"source": [
"classifier.compile(optimizer='Adam',loss='binary_crossentropy',metrics=['accuracy'])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "a5r5kr7xu59J",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "0c9c25d2-d622-4f3b-8739-f4a7f1c58ec5"
},
"source": [
"classifier.fit(X_train,y_train,batch_size=100,nb_epoch=100)"
],
"execution_count": 125,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
" \"\"\"Entry point for launching an IPython kernel.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Epoch 1/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.3960 - accuracy: 0.8430\n",
"Epoch 2/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.2038 - accuracy: 0.9637\n",
"Epoch 3/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.1668 - accuracy: 0.9634\n",
"Epoch 4/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.1532 - accuracy: 0.9611\n",
"Epoch 5/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.1480 - accuracy: 0.9603\n",
"Epoch 6/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.1460 - accuracy: 0.9595\n",
"Epoch 7/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.1452 - accuracy: 0.9592\n",
"Epoch 8/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.1448 - accuracy: 0.9592\n",
"Epoch 9/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.1427 - accuracy: 0.9589\n",
"Epoch 10/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.1379 - accuracy: 0.9589\n",
"Epoch 11/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.1370 - accuracy: 0.9591\n",
"Epoch 12/100\n",
"131027/131027 [==============================] - 5s 34us/step - loss: 0.1363 - accuracy: 0.9594\n",
"Epoch 13/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.1358 - accuracy: 0.9597\n",
"Epoch 14/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.1353 - accuracy: 0.9608\n",
"Epoch 15/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.1349 - accuracy: 0.9619\n",
"Epoch 16/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.1345 - accuracy: 0.9623\n",
"Epoch 17/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.1343 - accuracy: 0.9625\n",
"Epoch 18/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.1342 - accuracy: 0.9627\n",
"Epoch 19/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.1340 - accuracy: 0.9627\n",
"Epoch 20/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.1339 - accuracy: 0.9627\n",
"Epoch 21/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.1338 - accuracy: 0.9627\n",
"Epoch 22/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.1337 - accuracy: 0.9629\n",
"Epoch 23/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.1335 - accuracy: 0.9631\n",
"Epoch 24/100\n",
"131027/131027 [==============================] - 5s 38us/step - loss: 0.1334 - accuracy: 0.9633\n",
"Epoch 25/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.1333 - accuracy: 0.9635\n",
"Epoch 26/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.1331 - accuracy: 0.9635\n",
"Epoch 27/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.1330 - accuracy: 0.9635\n",
"Epoch 28/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.1330 - accuracy: 0.9636\n",
"Epoch 29/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.1330 - accuracy: 0.9637\n",
"Epoch 30/100\n",
"131027/131027 [==============================] - 5s 38us/step - loss: 0.1329 - accuracy: 0.9637\n",
"Epoch 31/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.1329 - accuracy: 0.9637\n",
"Epoch 32/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.1329 - accuracy: 0.9637\n",
"Epoch 33/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.1329 - accuracy: 0.9637\n",
"Epoch 34/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.1329 - accuracy: 0.9637\n",
"Epoch 35/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.1328 - accuracy: 0.9639\n",
"Epoch 36/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.1328 - accuracy: 0.9638\n",
"Epoch 37/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.1328 - accuracy: 0.9638\n",
"Epoch 38/100\n",
"131027/131027 [==============================] - 4s 34us/step - loss: 0.1328 - accuracy: 0.9638\n",
"Epoch 39/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.1327 - accuracy: 0.9639\n",
"Epoch 40/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.1327 - accuracy: 0.9638\n",
"Epoch 41/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.1327 - accuracy: 0.9640\n",
"Epoch 42/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.1321 - accuracy: 0.9639\n",
"Epoch 43/100\n",
"131027/131027 [==============================] - 5s 38us/step - loss: 0.1292 - accuracy: 0.9642\n",
"Epoch 44/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.1248 - accuracy: 0.9663\n",
"Epoch 45/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.1103 - accuracy: 0.9693\n",
"Epoch 46/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.0786 - accuracy: 0.9751\n",
"Epoch 47/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.0670 - accuracy: 0.9812\n",
"Epoch 48/100\n",
"131027/131027 [==============================] - 5s 38us/step - loss: 0.0625 - accuracy: 0.9839\n",
"Epoch 49/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.0597 - accuracy: 0.9845\n",
"Epoch 50/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0579 - accuracy: 0.9844\n",
"Epoch 51/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0563 - accuracy: 0.9844\n",
"Epoch 52/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.0551 - accuracy: 0.9846\n",
"Epoch 53/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.0541 - accuracy: 0.9845\n",
"Epoch 54/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.0534 - accuracy: 0.9847\n",
"Epoch 55/100\n",
"131027/131027 [==============================] - 5s 39us/step - loss: 0.0528 - accuracy: 0.9848\n",
"Epoch 56/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0523 - accuracy: 0.9847\n",
"Epoch 57/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0518 - accuracy: 0.9850\n",
"Epoch 58/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.0514 - accuracy: 0.9849\n",
"Epoch 59/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0511 - accuracy: 0.9846\n",
"Epoch 60/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.0507 - accuracy: 0.9849\n",
"Epoch 61/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.0505 - accuracy: 0.9849\n",
"Epoch 62/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0503 - accuracy: 0.9848\n",
"Epoch 63/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.0500 - accuracy: 0.9852\n",
"Epoch 64/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.0497 - accuracy: 0.9852\n",
"Epoch 65/100\n",
"131027/131027 [==============================] - 5s 38us/step - loss: 0.0497 - accuracy: 0.9854\n",
"Epoch 66/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0496 - accuracy: 0.9852\n",
"Epoch 67/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.0493 - accuracy: 0.9853\n",
"Epoch 68/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.0492 - accuracy: 0.9855\n",
"Epoch 69/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.0492 - accuracy: 0.9855\n",
"Epoch 70/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.0490 - accuracy: 0.9855\n",
"Epoch 71/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0488 - accuracy: 0.9853\n",
"Epoch 72/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0489 - accuracy: 0.9855\n",
"Epoch 73/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0488 - accuracy: 0.9855\n",
"Epoch 74/100\n",
"131027/131027 [==============================] - 5s 38us/step - loss: 0.0488 - accuracy: 0.9854\n",
"Epoch 75/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.0488 - accuracy: 0.9856\n",
"Epoch 76/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.0486 - accuracy: 0.9855\n",
"Epoch 77/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.0485 - accuracy: 0.9856\n",
"Epoch 78/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.0485 - accuracy: 0.9856\n",
"Epoch 79/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0484 - accuracy: 0.9856\n",
"Epoch 80/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0483 - accuracy: 0.9856\n",
"Epoch 81/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0483 - accuracy: 0.9856\n",
"Epoch 82/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.0482 - accuracy: 0.9857\n",
"Epoch 83/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0480 - accuracy: 0.9857\n",
"Epoch 84/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0481 - accuracy: 0.9858\n",
"Epoch 85/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.0479 - accuracy: 0.9858\n",
"Epoch 86/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0478 - accuracy: 0.9859\n",
"Epoch 87/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.0477 - accuracy: 0.9860\n",
"Epoch 88/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0477 - accuracy: 0.9862\n",
"Epoch 89/100\n",
"131027/131027 [==============================] - 5s 38us/step - loss: 0.0476 - accuracy: 0.9863\n",
"Epoch 90/100\n",
"131027/131027 [==============================] - 5s 39us/step - loss: 0.0476 - accuracy: 0.9865\n",
"Epoch 91/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.0476 - accuracy: 0.9865\n",
"Epoch 92/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.0475 - accuracy: 0.9867\n",
"Epoch 93/100\n",
"131027/131027 [==============================] - 5s 36us/step - loss: 0.0475 - accuracy: 0.9866\n",
"Epoch 94/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0475 - accuracy: 0.9866\n",
"Epoch 95/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0474 - accuracy: 0.9865\n",
"Epoch 96/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.0472 - accuracy: 0.9865\n",
"Epoch 97/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0473 - accuracy: 0.9867\n",
"Epoch 98/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0473 - accuracy: 0.9866\n",
"Epoch 99/100\n",
"131027/131027 [==============================] - 5s 35us/step - loss: 0.0472 - accuracy: 0.9866\n",
"Epoch 100/100\n",
"131027/131027 [==============================] - 5s 37us/step - loss: 0.0472 - accuracy: 0.9866\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.callbacks.callbacks.History at 0x7f6a902dce10>"
]
},
"metadata": {
"tags": []
},
"execution_count": 125
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ucotFZUYvmin",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 238
},
"outputId": "b365c782-fd4d-4f1d-f663-4800051b7908"
},
"source": [
"X_test\n"
],
"execution_count": 134,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [