diff --git a/docs/_data/umum.yml b/docs/_data/umum.yml index 9227456..3d6374f 100644 --- a/docs/_data/umum.yml +++ b/docs/_data/umum.yml @@ -47,3 +47,10 @@ date : 2019-07-30 author : taruma version : 1.0.0 + +- title : >- + Contoh Penggunaan AutoEncoder + notebook : taruma_udemy_autoencoders + date : 2019-08-01 + author : taruma + version : 1.0.0 diff --git a/notebook/taruma_udemy_autoencoders.ipynb b/notebook/taruma_udemy_autoencoders.ipynb new file mode 100644 index 0000000..3604999 --- /dev/null +++ b/notebook/taruma_udemy_autoencoders.ipynb @@ -0,0 +1,1216 @@ +{ + "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 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" + ], + "text/plain": [ + " 0 1 2 3\n", + "0 1 1193 5 978300760\n", + "1 1 661 3 978302109\n", + "2 1 914 3 978301968\n", + "3 1 3408 4 978300275\n", + "4 1 2355 5 978824291\n", + "5 1 1197 3 978302268\n", + "6 1 1287 5 978302039\n", + "7 1 2804 5 978300719\n", + "8 1 594 4 978302268\n", + "9 1 919 4 978301368" + ] + }, + "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: 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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" + } + ] + } + ] +} \ No newline at end of file