{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "taruma_udemy_autoencoders.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "lEhhFaw8YPqS",
"colab_type": "text"
},
"source": [
"# Auto Encoders\n",
"\n",
"Notebook ini berdasarkan kursus __Deep Learning A-Z™: Hands-On Artificial Neural Networks__ di Udemy. [Lihat Kursus](https://www.udemy.com/deeplearning/).\n",
"\n",
"## Informasi Notebook\n",
"- __notebook name__: `taruma_udemy_autoencoders`\n",
"- __notebook version/date__: `1.0.0`/`20190801`\n",
"- __notebook server__: Google Colab\n",
"- __python version__: `3.6`\n",
"- __pytorch version__: `1.1.0`\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "XgpbxDgHYPpL",
"colab_type": "code",
"outputId": "e6d8390d-a34d-4414-81da-1bd5fc717c95",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
}
},
"source": [
"#### NOTEBOOK DESCRIPTION\n",
"\n",
"from datetime import datetime\n",
"\n",
"NOTEBOOK_TITLE = 'taruma_udemy_autoencoders'\n",
"NOTEBOOK_VERSION = '1.0.0'\n",
"NOTEBOOK_DATE = 1 # Set 1, if you want add date classifier\n",
"\n",
"NOTEBOOK_NAME = \"{}_{}\".format(\n",
" NOTEBOOK_TITLE, \n",
" NOTEBOOK_VERSION.replace('.','_')\n",
")\n",
"PROJECT_NAME = \"{}_{}{}\".format(\n",
" NOTEBOOK_TITLE, \n",
" NOTEBOOK_VERSION.replace('.','_'), \n",
" \"_\" + datetime.utcnow().strftime(\"%Y%m%d_%H%M\") if NOTEBOOK_DATE else \"\"\n",
")\n",
"\n",
"print(f\"Nama Notebook: {NOTEBOOK_NAME}\")\n",
"print(f\"Nama Proyek: {PROJECT_NAME}\")"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Nama Notebook: taruma_udemy_autoencoders_1_0_0\n",
"Nama Proyek: taruma_udemy_autoencoders_1_0_0_20190801_0925\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ht_fonylY0my",
"colab_type": "code",
"outputId": "90c170d9-ac70-4562-be35-9f30401bd780",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 68
}
},
"source": [
"#### System Version\n",
"import sys, torch\n",
"print(\"versi python: {}\".format(sys.version))\n",
"print(\"versi pytorch: {}\".format(torch.__version__))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"versi python: 3.6.8 (default, Jan 14 2019, 11:02:34) \n",
"[GCC 8.0.1 20180414 (experimental) [trunk revision 259383]]\n",
"versi pytorch: 1.1.0\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "wWHOjSRRY5Pf",
"colab_type": "code",
"colab": {}
},
"source": [
"#### Load Notebook Extensions\n",
"%load_ext google.colab.data_table"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "sS6B-Y06Y8UB",
"colab_type": "code",
"outputId": "a20211fd-9b95-4f7c-82d8-e191ac2b1d97",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 340
}
},
"source": [
"#### Download dataset\n",
"# ref: https://grouplens.org/datasets/movielens/\n",
"!wget -O autoencoders.zip \"https://sds-platform-private.s3-us-east-2.amazonaws.com/uploads/P16-AutoEncoders.zip\"\n",
"!unzip autoencoders.zip"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"--2019-08-01 09:25:40-- https://sds-platform-private.s3-us-east-2.amazonaws.com/uploads/P16-AutoEncoders.zip\n",
"Resolving sds-platform-private.s3-us-east-2.amazonaws.com (sds-platform-private.s3-us-east-2.amazonaws.com)... 52.219.80.168\n",
"Connecting to sds-platform-private.s3-us-east-2.amazonaws.com (sds-platform-private.s3-us-east-2.amazonaws.com)|52.219.80.168|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 17069342 (16M) [application/zip]\n",
"Saving to: ‘autoencoders.zip’\n",
"\n",
"autoencoders.zip 100%[===================>] 16.28M 34.2MB/s in 0.5s \n",
"\n",
"2019-08-01 09:25:40 (34.2 MB/s) - ‘autoencoders.zip’ saved [17069342/17069342]\n",
"\n",
"Archive: autoencoders.zip\n",
" creating: AutoEncoders/\n",
" inflating: AutoEncoders/ae.py \n",
" creating: __MACOSX/\n",
" creating: __MACOSX/AutoEncoders/\n",
" inflating: __MACOSX/AutoEncoders/._ae.py \n",
" inflating: AutoEncoders/ml-100k.zip \n",
" inflating: AutoEncoders/ml-1m.zip \n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "jm-eeDVwcAda",
"colab_type": "code",
"outputId": "445a373d-bebf-418b-fb6c-ec639956afb1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"# Karena ada file .zip dalam direktori, harus diekstrak lagi.\n",
"# ref: https://askubuntu.com/q/399951\n",
"# ref: https://unix.stackexchange.com/q/12902\n",
"!find AutoEncoders -type f -name '*.zip' -exec unzip -d AutoEncoders {} \\;"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Archive: AutoEncoders/ml-100k.zip\n",
" creating: AutoEncoders/ml-100k/\n",
" inflating: AutoEncoders/ml-100k/allbut.pl \n",
" creating: AutoEncoders/__MACOSX/\n",
" creating: AutoEncoders/__MACOSX/ml-100k/\n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._allbut.pl \n",
" inflating: AutoEncoders/ml-100k/mku.sh \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._mku.sh \n",
" inflating: AutoEncoders/ml-100k/README \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._README \n",
" inflating: AutoEncoders/ml-100k/u.data \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._u.data \n",
" inflating: AutoEncoders/ml-100k/u.genre \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._u.genre \n",
" inflating: AutoEncoders/ml-100k/u.info \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._u.info \n",
" inflating: AutoEncoders/ml-100k/u.item \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._u.item \n",
" inflating: AutoEncoders/ml-100k/u.occupation \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._u.occupation \n",
" inflating: AutoEncoders/ml-100k/u.user \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._u.user \n",
" inflating: AutoEncoders/ml-100k/u1.base \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._u1.base \n",
" inflating: AutoEncoders/ml-100k/u1.test \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._u1.test \n",
" inflating: AutoEncoders/ml-100k/u2.base \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._u2.base \n",
" inflating: AutoEncoders/ml-100k/u2.test \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._u2.test \n",
" inflating: AutoEncoders/ml-100k/u3.base \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._u3.base \n",
" inflating: AutoEncoders/ml-100k/u3.test \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._u3.test \n",
" inflating: AutoEncoders/ml-100k/u4.base \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._u4.base \n",
" inflating: AutoEncoders/ml-100k/u4.test \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._u4.test \n",
" inflating: AutoEncoders/ml-100k/u5.base \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._u5.base \n",
" inflating: AutoEncoders/ml-100k/u5.test \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._u5.test \n",
" inflating: AutoEncoders/ml-100k/ua.base \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._ua.base \n",
" inflating: AutoEncoders/ml-100k/ua.test \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._ua.test \n",
" inflating: AutoEncoders/ml-100k/ub.base \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._ub.base \n",
" inflating: AutoEncoders/ml-100k/ub.test \n",
" inflating: AutoEncoders/__MACOSX/ml-100k/._ub.test \n",
" inflating: AutoEncoders/__MACOSX/._ml-100k \n",
"Archive: AutoEncoders/ml-1m.zip\n",
" creating: AutoEncoders/ml-1m/\n",
" inflating: AutoEncoders/ml-1m/.DS_Store \n",
" creating: AutoEncoders/__MACOSX/ml-1m/\n",
" inflating: AutoEncoders/__MACOSX/ml-1m/._.DS_Store \n",
" inflating: AutoEncoders/ml-1m/.Rhistory \n",
" inflating: AutoEncoders/ml-1m/movies.dat \n",
" inflating: AutoEncoders/__MACOSX/ml-1m/._movies.dat \n",
" inflating: AutoEncoders/ml-1m/ratings.dat \n",
" inflating: AutoEncoders/__MACOSX/ml-1m/._ratings.dat \n",
" inflating: AutoEncoders/ml-1m/README \n",
" inflating: AutoEncoders/__MACOSX/ml-1m/._README \n",
" inflating: AutoEncoders/ml-1m/test_set.csv \n",
" inflating: AutoEncoders/__MACOSX/ml-1m/._test_set.csv \n",
" inflating: AutoEncoders/ml-1m/training_set.csv \n",
" inflating: AutoEncoders/__MACOSX/ml-1m/._training_set.csv \n",
" inflating: AutoEncoders/ml-1m/users.dat \n",
" inflating: AutoEncoders/__MACOSX/ml-1m/._users.dat \n",
" inflating: AutoEncoders/__MACOSX/._ml-1m \n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "q1oOGi4jZYrp",
"colab_type": "code",
"colab": {}
},
"source": [
"#### Atur dataset path\n",
"DATASET_DIRECTORY = 'AutoEncoders/'"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "1aWLNovwgC_X",
"colab_type": "code",
"colab": {}
},
"source": [
"def showdata(dataframe):\n",
" print('Dataframe Size: {}'.format(dataframe.shape))\n",
" return dataframe"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "hqE6ozW8e0ra",
"colab_type": "text"
},
"source": [
"# STEP 1-5 DATA PREPROCESSING"
]
},
{
"cell_type": "code",
"metadata": {
"id": "fLvxd5pQdTQq",
"colab_type": "code",
"colab": {}
},
"source": [
"# Importing the libraries\n",
"import numpy as np\n",
"import pandas as pd\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.parallel\n",
"import torch.optim as optim\n",
"import torch.utils.data\n",
"from torch.autograd import Variable"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "lFQEACh4fJLp",
"colab_type": "code",
"outputId": "cc5e7095-91f4-4594-f30f-cf2f41380595",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 309
}
},
"source": [
"movies = pd.read_csv(DATASET_DIRECTORY + 'ml-1m/movies.dat', sep='::', header=None, engine='python', encoding='latin-1')\n",
"showdata(movies).head(10)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Dataframe Size: (3883, 3)\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/81868506e94e6988/data_table.js\";\n\n window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n\"Toy Story (1995)\",\n\"Animation|Children's|Comedy\"],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n\"Jumanji (1995)\",\n\"Adventure|Children's|Fantasy\"],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 3,\n 'f': \"3\",\n },\n\"Grumpier Old Men (1995)\",\n\"Comedy|Romance\"],\n [{\n 'v': 3,\n 'f': \"3\",\n },\n{\n 'v': 4,\n 'f': \"4\",\n },\n\"Waiting to Exhale (1995)\",\n\"Comedy|Drama\"],\n [{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 5,\n 'f': \"5\",\n },\n\"Father of the Bride Part II (1995)\",\n\"Comedy\"],\n [{\n 'v': 5,\n 'f': \"5\",\n },\n{\n 'v': 6,\n 'f': \"6\",\n },\n\"Heat (1995)\",\n\"Action|Crime|Thriller\"],\n [{\n 'v': 6,\n 'f': \"6\",\n },\n{\n 'v': 7,\n 'f': \"7\",\n },\n\"Sabrina (1995)\",\n\"Comedy|Romance\"],\n [{\n 'v': 7,\n 'f': \"7\",\n },\n{\n 'v': 8,\n 'f': \"8\",\n },\n\"Tom and Huck (1995)\",\n\"Adventure|Children's\"],\n [{\n 'v': 8,\n 'f': \"8\",\n },\n{\n 'v': 9,\n 'f': \"9\",\n },\n\"Sudden Death (1995)\",\n\"Action\"],\n [{\n 'v': 9,\n 'f': \"9\",\n },\n{\n 'v': 10,\n 'f': \"10\",\n },\n\"GoldenEye (1995)\",\n\"Action|Adventure|Thriller\"]],\n columns: [[\"number\", \"index\"], [\"number\", \"0\"], [\"string\", \"1\"], [\"string\", \"2\"]],\n rowsPerPage: 25,\n });\n ",
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"users = pd.read_csv(DATASET_DIRECTORY + 'ml-1m/users.dat', sep='::', header=None, engine='python', encoding='latin-1')\n",
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],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
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],
"name": "stdout"
},
{
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},
"metadata": {
"tags": []
},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "anc5Oi1sgDzc",
"colab_type": "code",
"outputId": "295da240-b2dc-467a-f9c3-8c7ad3e1756c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 309
}
},
"source": [
"ratings = pd.read_csv(DATASET_DIRECTORY + 'ml-1m/ratings.dat', sep='::', header=None, engine='python', encoding='latin-1')\n",
"showdata(ratings).head(10)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Dataframe Size: (1000209, 4)\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/81868506e94e6988/data_table.js\";\n\n window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 1193,\n 'f': \"1193\",\n },\n{\n 'v': 5,\n 'f': \"5\",\n },\n{\n 'v': 978300760,\n 'f': \"978300760\",\n }],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 661,\n 'f': \"661\",\n },\n{\n 'v': 3,\n 'f': \"3\",\n },\n{\n 'v': 978302109,\n 'f': \"978302109\",\n }],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 914,\n 'f': \"914\",\n },\n{\n 'v': 3,\n 'f': \"3\",\n },\n{\n 'v': 978301968,\n 'f': \"978301968\",\n }],\n [{\n 'v': 3,\n 'f': \"3\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 3408,\n 'f': \"3408\",\n },\n{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 978300275,\n 'f': \"978300275\",\n }],\n [{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 2355,\n 'f': \"2355\",\n },\n{\n 'v': 5,\n 'f': \"5\",\n },\n{\n 'v': 978824291,\n 'f': \"978824291\",\n }],\n [{\n 'v': 5,\n 'f': \"5\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 1197,\n 'f': \"1197\",\n },\n{\n 'v': 3,\n 'f': \"3\",\n },\n{\n 'v': 978302268,\n 'f': \"978302268\",\n }],\n [{\n 'v': 6,\n 'f': \"6\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 1287,\n 'f': \"1287\",\n },\n{\n 'v': 5,\n 'f': \"5\",\n },\n{\n 'v': 978302039,\n 'f': \"978302039\",\n }],\n [{\n 'v': 7,\n 'f': \"7\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 2804,\n 'f': \"2804\",\n },\n{\n 'v': 5,\n 'f': \"5\",\n },\n{\n 'v': 978300719,\n 'f': \"978300719\",\n }],\n [{\n 'v': 8,\n 'f': \"8\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 594,\n 'f': \"594\",\n },\n{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 978302268,\n 'f': \"978302268\",\n }],\n [{\n 'v': 9,\n 'f': \"9\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 919,\n 'f': \"919\",\n },\n{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 978301368,\n 'f': \"978301368\",\n }]],\n columns: [[\"number\", \"index\"], [\"number\", \"0\"], [\"number\", \"1\"], [\"number\", \"2\"], [\"number\", \"3\"]],\n rowsPerPage: 25,\n });\n ",
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},
"metadata": {
"tags": []
},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "xU2S9y8NgRPW",
"colab_type": "code",
"colab": {}
},
"source": [
"# Preparing the training set and the test set\n",
"training_set = pd.read_csv(DATASET_DIRECTORY + 'ml-100k/u1.base', delimiter='\\t')\n",
"training_set = np.array(training_set, dtype='int')\n",
"test_set = pd.read_csv(DATASET_DIRECTORY + 'ml-100k/u1.test', delimiter='\\t')\n",
"test_set = np.array(test_set, dtype='int')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "G7WbrddJl3Q9",
"colab_type": "code",
"colab": {}
},
"source": [
"# Getting the number of users and movies\n",
"nb_users = int(max(max(training_set[:, 0]), max(test_set[:, 0])))\n",
"nb_movies = int(max(max(training_set[:, 1]), max(test_set[:, 1])))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "yRUTR_K3_rzP",
"colab_type": "code",
"colab": {}
},
"source": [
"# Converting the data into an array with users in lines and movies in columns\n",
"def convert(data):\n",
" new_data = []\n",
" for id_users in range(1, nb_users+1):\n",
" id_movies = data[:, 1][data[:, 0] == id_users]\n",
" id_ratings = data[:, 2][data[:, 0] == id_users]\n",
" ratings = np.zeros(nb_movies)\n",
" ratings[id_movies - 1] = id_ratings\n",
" new_data.append(list(ratings))\n",
" return new_data\n",
"\n",
"training_set = convert(training_set)\n",
"test_set = convert(test_set)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "u0Fk8Q0YCNZr",
"colab_type": "code",
"colab": {}
},
"source": [
"# Converting the data into Torch tensors\n",
"training_set = torch.FloatTensor(training_set)\n",
"test_set = torch.FloatTensor(test_set)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "q2arIJufDBYd",
"colab_type": "code",
"outputId": "41675f96-4fb9-40cb-d97c-54d983455e8b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 136
}
},
"source": [
"training_set"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"tensor([[0., 3., 4., ..., 0., 0., 0.],\n",
" [4., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" ...,\n",
" [5., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 5., 0., ..., 0., 0., 0.]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 17
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qAkHvP9NhOPp",
"colab_type": "text"
},
"source": [
"# STEP 6-7"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Bz95oUacgQPQ",
"colab_type": "code",
"colab": {}
},
"source": [
"# Creating the architecture of the Neural Network\n",
"class SAE(nn.Module):\n",
" def __init__(self, ):\n",
" super(SAE, self).__init__()\n",
" self.fc1 = nn.Linear(nb_movies, 20)\n",
" self.fc2 = nn.Linear(20, 10)\n",
" self.fc3 = nn.Linear(10, 20)\n",
" self.fc4 = nn.Linear(20, nb_movies)\n",
" self.activation = nn.Sigmoid()\n",
" def forward(self, x):\n",
" x = self.activation(self.fc1(x))\n",
" x = self.activation(self.fc2(x))\n",
" x = self.activation(self.fc3(x))\n",
" x = self.fc4(x)\n",
" return x\n",
"sae = SAE()\n",
"criterion = nn.MSELoss()\n",
"optimizer = optim.RMSprop(sae.parameters(), lr = 0.01, weight_decay = 0.5)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "nuM-A8Ozjty4",
"colab_type": "text"
},
"source": [
"# STEP 8-10"
]
},
{
"cell_type": "code",
"metadata": {
"id": "xiYO03QMjsHG",
"colab_type": "code",
"outputId": "f160c864-896f-4f52-e994-2a4fddfbc307",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"# Training the SAE\n",
"nb_epoch = 200\n",
"for epoch in range(1, nb_epoch + 1):\n",
" train_loss = 0\n",
" s = 0.\n",
" for id_user in range(nb_users):\n",
" input = Variable(training_set[id_user]).unsqueeze(0)\n",
" target = input.clone()\n",
" if torch.sum(target.data > 0) > 0:\n",
" output = sae(input)\n",
" target.require_grad = False\n",
" output[target == 0] = 0\n",
" loss = criterion(output, target)\n",
" mean_corrector = nb_movies/float(torch.sum(target.data > 0) + 1e-10)\n",
" loss.backward()\n",
" train_loss += np.sqrt(loss.item()*mean_corrector)\n",
" s += 1.\n",
" optimizer.step()\n",
" print('epoch: '+str(epoch)+' loss: '+str(train_loss/s))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"epoch: 1 loss: 1.7663983791313438\n",
"epoch: 2 loss: 1.0965944818481448\n",
"epoch: 3 loss: 1.0533398732955221\n",
"epoch: 4 loss: 1.0383018413922185\n",
"epoch: 5 loss: 1.0308177439541621\n",
"epoch: 6 loss: 1.026551124053685\n",
"epoch: 7 loss: 1.023840092408676\n",
"epoch: 8 loss: 1.021978586980373\n",
"epoch: 9 loss: 1.0206570638587025\n",
"epoch: 10 loss: 1.0196462708959995\n",
"epoch: 11 loss: 1.0187753163243505\n",
"epoch: 12 loss: 1.018512555740381\n",
"epoch: 13 loss: 1.0178744683018195\n",
"epoch: 14 loss: 1.0174755647701952\n",
"epoch: 15 loss: 1.0170719470478082\n",
"epoch: 16 loss: 1.017201642832892\n",
"epoch: 17 loss: 1.0163239136444078\n",
"epoch: 18 loss: 1.0165747767066637\n",
"epoch: 19 loss: 1.0162508415906395\n",
"epoch: 20 loss: 1.0162299744574526\n",
"epoch: 21 loss: 1.0160825599663328\n",
"epoch: 22 loss: 1.0159708620648906\n",
"epoch: 23 loss: 1.0159037432204494\n",
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"epoch: 25 loss: 1.0156815102111703\n",
"epoch: 26 loss: 1.0154590358153581\n",
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"epoch: 152 loss: 0.9221839934603951\n",
"epoch: 153 loss: 0.9227788564070573\n",
"epoch: 154 loss: 0.9213350301333955\n",
"epoch: 155 loss: 0.922453842482827\n",
"epoch: 156 loss: 0.9210483122507049\n",
"epoch: 157 loss: 0.9219510963958538\n",
"epoch: 158 loss: 0.9204969614260258\n",
"epoch: 159 loss: 0.9205394209501664\n",
"epoch: 160 loss: 0.9200661759022467\n",
"epoch: 161 loss: 0.9207735137229326\n",
"epoch: 162 loss: 0.9196641402017643\n",
"epoch: 163 loss: 0.9204513049820104\n",
"epoch: 164 loss: 0.9193051927516236\n",
"epoch: 165 loss: 0.9210140873158912\n",
"epoch: 166 loss: 0.9193127515207875\n",
"epoch: 167 loss: 0.9200597882686071\n",
"epoch: 168 loss: 0.9185944485414366\n",
"epoch: 169 loss: 0.9201572432142742\n",
"epoch: 170 loss: 0.9183169550351225\n",
"epoch: 171 loss: 0.9193881788559667\n",
"epoch: 172 loss: 0.9180057668314479\n",
"epoch: 173 loss: 0.9191220927901347\n",
"epoch: 174 loss: 0.9177848844173945\n",
"epoch: 175 loss: 0.9190516442024842\n",
"epoch: 176 loss: 0.9181445924423348\n",
"epoch: 177 loss: 0.919047934578481\n",
"epoch: 178 loss: 0.9175119757656524\n",
"epoch: 179 loss: 0.9186781150882567\n",
"epoch: 180 loss: 0.9175681590539049\n",
"epoch: 181 loss: 0.9183763375326187\n",
"epoch: 182 loss: 0.9169434621528899\n",
"epoch: 183 loss: 0.9177548550969366\n",
"epoch: 184 loss: 0.9170545570415128\n",
"epoch: 185 loss: 0.9179762411576573\n",
"epoch: 186 loss: 0.9166707151557505\n",
"epoch: 187 loss: 0.9174266883043443\n",
"epoch: 188 loss: 0.9162146914993445\n",
"epoch: 189 loss: 0.917265776286358\n",
"epoch: 190 loss: 0.9159440051014004\n",
"epoch: 191 loss: 0.9167926651895048\n",
"epoch: 192 loss: 0.9157365677088328\n",
"epoch: 193 loss: 0.9169038115550036\n",
"epoch: 194 loss: 0.9156644022282158\n",
"epoch: 195 loss: 0.916360655268448\n",
"epoch: 196 loss: 0.9149874787609436\n",
"epoch: 197 loss: 0.9160702331415719\n",
"epoch: 198 loss: 0.9148375459877753\n",
"epoch: 199 loss: 0.915890166240895\n",
"epoch: 200 loss: 0.9151742022378695\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Egrl1Ge4kA6Y",
"colab_type": "code",
"outputId": "f5d79f27-2a67-4498-ca86-ab8d6fb88240",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# Testing the SAE\n",
"test_loss = 0\n",
"s = 0.\n",
"for id_user in range(nb_users):\n",
" input = Variable(training_set[id_user]).unsqueeze(0)\n",
" target = Variable(test_set[id_user]).unsqueeze(0)\n",
" if torch.sum(target.data > 0) > 0:\n",
" output = sae(input)\n",
" target.require_grad = False\n",
" output[target == 0] = 0\n",
" loss = criterion(output, target)\n",
" mean_corrector = nb_movies/float(torch.sum(target.data > 0) + 1e-10)\n",
" test_loss += np.sqrt(loss.item()*mean_corrector)\n",
" s += 1.\n",
"print('test loss: '+str(test_loss/s))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"test loss: 0.9503542203018388\n"
],
"name": "stdout"
}
]
}
]
}