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TFRS Two-tower Retrieval Model on ML-1m"]},{"cell_type":"markdown","metadata":{"id":"b50uwaHSPw-C"},"source":["## Setup"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oUcRd9PPKihx","executionInfo":{"status":"ok","timestamp":1636018107771,"user_tz":-330,"elapsed":4567,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"e829c62c-7db8-4cf9-e146-b8f6d7c51dab"},"source":["!pip install -q tensorflow_recommenders"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[?25l\r\u001b[K     |███▉                            | 10 kB 18.1 MB/s eta 0:00:01\r\u001b[K     |███████▋                        | 20 kB 22.5 MB/s eta 0:00:01\r\u001b[K     |███████████▌                    | 30 kB 12.9 MB/s eta 0:00:01\r\u001b[K     |███████████████▎                | 40 kB 9.9 MB/s eta 0:00:01\r\u001b[K     |███████████████████             | 51 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████         | 61 kB 5.7 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▊     | 71 kB 6.1 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▌ | 81 kB 6.8 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 85 kB 550 kB/s \n","\u001b[?25h"]}]},{"cell_type":"code","metadata":{"id":"zy82mzsUKdwv"},"source":["import os\n","import pprint\n","import tempfile\n","import matplotlib.pyplot as plt\n","from typing import Dict, Text\n","\n","import numpy as np\n","import tensorflow as tf\n","from typing import Dict, Text\n","import pandas as pd\n","import numpy as np\n","\n","import tensorflow as tf\n","\n","import tensorflow_datasets as tfds\n","import tensorflow_recommenders as tfrs\n","\n","plt.style.use('ggplot')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Oo9aZsTfPyUe"},"source":["## Data Loading and Processing"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":497,"referenced_widgets":["6f659f29afcd45c88e58b85cd7589510","00b3d8c2963b4bcaa63289460c2a29a7","c91d3454b3d945d2a4f7a839653f5f9e","90620c1f337144538797b724cb989541","7ceb905e45da4498872ec3c176f9925e","45131df318e042809a1c803a6a8ea915","723538beb5004d0da0b6cc09a39ee53e","9e699982db094ab19a8497c250ff7800","982f1678089741c0b4e8f953fc4c644f","a4cf1074c4084d05bdc8e4052ffccce3","83833ed236924204b967c22db0bf09e6","890e1a63a9ce4333a73b949300835306","1437ebc9209d4669a2048535030a45d8","d6908d60b08d42fc96609570070e86ab","7cdab38d07434ebdbd23d91bfc260974","066387f9c71645089a372da18e954f49","6e77e96b53e64627ba94bc335a5d0989","6cf311e0d0254353a1113421d3cea621","86f3a2527e6e4e2ba88a0a4869b84a7a","7313ada0386f428c812846de27786e64","7929ea9cc93f4009a2165c888215fdc4","c4f39b92db4d4efea5bb2417fc174626","08ae082590e14602a5390c4b1579050b","f0bda57e9b1849ef9818d841d7eff301","7115b065bcf84b74b2bcc5ec793bf938","5966267b1bea47cfba70787850f530a6","ab6f803d2e6e4facb206a27017f191ee","344d4405dea3482684b6a72f6066f2cd","476fdd686f8f46f0ba876301e5db1a43","3bb0a3af0252436e90357197719fb522","8646826de0134b78bff98794a1e1e254","b1c246c735cd4631a69dae51f96afb22","30b0cd873514424a9cef14e9392a5106","fecb782718fb4bea869c3b71c6d0458b","3dd51c39d5dd44d29bb24e9b068d016e","6c2781a64ea44761bf546683b4584684","a243d94a27f24504a8f339b0c365a141","ddf61d3c70844c2487e8cf2a0dd78ae4","5a117b5b00c34939b080ca1c5ed5366e","ee4854d0d895413693f9d7be753d495a","b63e43df68934fdb86563942b5199a13","b5bb151def9b455cb83c758f09d3c7a5","15c9b93815a141a59d3f0e0fbc8e2e71","385c8c38a595477099210b80ee4d3c0a","3bd6f957b63842d386d4bd1aaee5d4e8","ad0ae077e31c4387a5f217d0d2c49876","23c94bed3e2b4d5b96bd5bb80af4c7a6","0c2f15aa6553430c957903714b09acaf","b6e11a3ccc3a46ca81e0e5567372db0b","17534eeb9b8948c2a4fb81bf68e2a094","c4226d0a5cc046d181836d32eb2ef643","668faa607dc8437e91a628c3c6d838d7","6a775e4fb3f843528993116b95d2f969","63ebd9d5b3e84ebc932856ad8384a509","38f0ad9615eb4516b2bbd35cab7ccee8","c394520c795f48d0a079d9e14047e512","ebd129626ff142099b951d98d6e0f1e5","685ff51d2bd84b92921c30d15b690d8d","d8db6cd2b97e43b6bbe528c1cf78efb6","f0d6e96ede0e47c0ad4c0632acfb98a3","0a9dcfa143e3497297a792b053ff382b","73cf3a33ca424b359e5d710307af4390","c36852f3b4734def85d070c122543832","8cdb28d8803049f59ea5f82c92da418d","132f46a804424e74885baabfe7c773d4","91d328ee8cb540ca97b48d0aa684d21d","0bc9953451c44707bd33fada7ceb9112","2dd7226bc73c4721a0b98b2a547d370f","4cad39ee52cb4cb5b229faf83b9ffbef","41b6f84bb8d74ad893d25dfc99fd519c","507f7b8b10584f4ca2b90a9c4dfe29b7","bd8d78b143714e9f802b5c4bbf40c232","803dd352a7a04bc4894201e38bf51eb2","3800494bcccc46428e1f20c446fe28bd","51f309a9cb4a4bffa61f27e314d22878","cfb64d85f93c403e8476a0725017df93","7ee751784d804a70a3d25b170a7b49a6","c7cd890b95164102afbdb6e6388eb4c3","bef38e8beaa74a089900757017be5809","7a7853a145304ba5a6b2a7ad392237a3","91e0eec763f3489691f091f2ea0b7eb9","28d0d2b439ca4824b003294c26af03f4","9008ce61d6c14194843a5231c2b934ff","8ad8cef750a144728512b8825d2ae2f4","7ba9ff85a8f343f08cd63a047c89cd82","ad72eab6ae214df18f9019fa41cc8c63","8943a5af11164f89b72660f419010fcd","8e9c50ab283547c28f9cca43bbe9b6f8","4568c707075d412892c85c4dbf5ad93b","39bacdd2f02241e585a1a4de1223554f","bfbed280ed794e3fa74ef7aac7f0151d","7f2f806290cc4f45930c8c9771a11e9b","f563333bf05149c6aa8f63da775eed47","bee34a54733d4db085d84a715fda691c","ae6d23ee3bdd4030871b394dde281ae0","c357c79a96bd41cabcaf47e3f87d7993","d681df83c3be4c838497fa631051a7fc","83fd258db2f144bcb506454dad8df744","096b27dfa13e446fa8eb764163360aab","583dc08479954008959d001ced8b25bc","641a8493c78e4e56acc90d94b9ea1703","eee86b97f62e48aa8b7b72f4e2306c7f","1bcfd65aba2341269f3aebcb208a76b8","de1a81082638418399bf57890ad49638","2aaf5f5d0b12498f9add61c8085d4073","5d682ece209d4c2092d85ed859dae02f","d3d2b222828e4e48a4d63f07c6f251bd","2479e66fdd23473b9cc332ebd156bf17","fae53a890b384edf92e521e8d1efb099","dfd3b730e8014ccab1bdb53e5a44595b"]},"id":"KuRJ9EHuKxIA","executionInfo":{"status":"ok","timestamp":1636019437486,"user_tz":-330,"elapsed":1326826,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"3c4f3ebf-f7b0-4164-8919-235b732daaad"},"source":["# Ratings data.\n","ratings = tfds.load(\"movielens/1m-ratings\", split=\"train\", shuffle_files=True)\n","# Features of all the available movies.\n","movies = tfds.load(\"movielens/1m-movies\", split=\"train\", shuffle_files=True)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1mDownloading and preparing dataset movielens/1m-ratings/0.1.0 (download: 5.64 MiB, generated: 308.42 MiB, total: 314.06 MiB) to /root/tensorflow_datasets/movielens/1m-ratings/0.1.0...\u001b[0m\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"6f659f29afcd45c88e58b85cd7589510","version_minor":0,"version_major":2},"text/plain":["Dl Completed...: 0 url [00:00, ? url/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"890e1a63a9ce4333a73b949300835306","version_minor":0,"version_major":2},"text/plain":["Dl Size...: 0 MiB [00:00, ? MiB/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"08ae082590e14602a5390c4b1579050b","version_minor":0,"version_major":2},"text/plain":["Extraction completed...: 0 file [00:00, ? file/s]"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","\n","\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"fecb782718fb4bea869c3b71c6d0458b","version_minor":0,"version_major":2},"text/plain":["0 examples [00:00, ? examples/s]"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Shuffling and writing examples to /root/tensorflow_datasets/movielens/1m-ratings/0.1.0.incomplete4B0PBX/movielens-train.tfrecord\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"3bd6f957b63842d386d4bd1aaee5d4e8","version_minor":0,"version_major":2},"text/plain":["  0%|          | 0/1000209 [00:00<?, ? examples/s]"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\u001b[1mDataset movielens downloaded and prepared to /root/tensorflow_datasets/movielens/1m-ratings/0.1.0. Subsequent calls will reuse this data.\u001b[0m\n","\u001b[1mDownloading and preparing dataset movielens/1m-movies/0.1.0 (download: 5.64 MiB, generated: 351.12 KiB, total: 5.99 MiB) to /root/tensorflow_datasets/movielens/1m-movies/0.1.0...\u001b[0m\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c394520c795f48d0a079d9e14047e512","version_minor":0,"version_major":2},"text/plain":["Dl Completed...: 0 url [00:00, ? url/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0bc9953451c44707bd33fada7ceb9112","version_minor":0,"version_major":2},"text/plain":["Dl Size...: 0 MiB [00:00, ? MiB/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c7cd890b95164102afbdb6e6388eb4c3","version_minor":0,"version_major":2},"text/plain":["Extraction completed...: 0 file [00:00, ? file/s]"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","\n","\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4568c707075d412892c85c4dbf5ad93b","version_minor":0,"version_major":2},"text/plain":["0 examples [00:00, ? examples/s]"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Shuffling and writing examples to /root/tensorflow_datasets/movielens/1m-movies/0.1.0.incomplete9YE8X5/movielens-train.tfrecord\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"583dc08479954008959d001ced8b25bc","version_minor":0,"version_major":2},"text/plain":["  0%|          | 0/3883 [00:00<?, ? examples/s]"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\u001b[1mDataset movielens downloaded and prepared to /root/tensorflow_datasets/movielens/1m-movies/0.1.0. Subsequent calls will reuse this data.\u001b[0m\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9aL1dnCULANK","executionInfo":{"status":"ok","timestamp":1636019439728,"user_tz":-330,"elapsed":738,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"af3e3b13-7f39-4474-889d-b02cf2e61957"},"source":["#The ratings dataset returns a dictionary of movie id, user id, the assigned rating, timestamp, movie information, and user information:\n","#View the data from ratings dataset:\n","for x in ratings.take(1).as_numpy_iterator():\n","    pprint.pprint(x)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'bucketized_user_age': 45.0,\n"," 'movie_genres': array([ 5, 16]),\n"," 'movie_id': b'2391',\n"," 'movie_title': b'Simple Plan, A (1998)',\n"," 'timestamp': 967497374,\n"," 'user_gender': False,\n"," 'user_id': b'3483',\n"," 'user_occupation_label': 6,\n"," 'user_occupation_text': b'executive/managerial',\n"," 'user_rating': 4.0,\n"," 'user_zip_code': b'30260'}\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MbBj-IHEL1zI","executionInfo":{"status":"ok","timestamp":1636019440462,"user_tz":-330,"elapsed":6,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"5cf3aadd-3cbb-4179-fe6a-b4d5980ff96a"},"source":["#The movies dataset contains the movie id, movie title, and data on what genres it belongs to. Note that the genres are encoded with integer labels:\n","#View the data from movies dataset:\n","for x in movies.take(1).as_numpy_iterator():\n","    pprint.pprint(x)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'movie_genres': array([5, 7]),\n"," 'movie_id': b'1729',\n"," 'movie_title': b'Jackie Brown (1997)'}\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TNI9_ie4L2lD","executionInfo":{"status":"ok","timestamp":1636019441154,"user_tz":-330,"elapsed":10,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"0d2734ac-7f80-4774-e897-1ba0de473e73"},"source":["type(ratings)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensorflow.python.data.ops.dataset_ops._OptionsDataset"]},"metadata":{},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"Z69RMmvHKag6"},"source":["We're only going to extract the movie title and the user id. So, we're actually not going to extract the rating itself and the reason why is that we're treating these as implicit recommendations in this case because that's easier to do a large scale like we mentioned earlier we want to try to keep things simple in the retrieval stage because it's operating over potentially a massive amount of data so to that end we're just going to assume that any movie that a user rated is one that they were really interested in if they took the time to watch it then it expresses some level of interest:"]},{"cell_type":"code","metadata":{"id":"GwQevswBKv4y"},"source":["#Let's select the necessary attributes:\n","\n","ratings = ratings.map(lambda x: {\n","                                 \"movie_title\": x[\"movie_title\"],\n","                                 \"user_id\": x[\"user_id\"],\n","                                })\n","\n","movies = movies.map(lambda x: x[\"movie_title\"])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"gsY0gEkOLCcn"},"source":["# let's use a random split, putting 75% of the ratings in the train set, and 25% in the test set:\n","# Assign a seed=42 for consistency of results and reproducibility:\n","seed = 42\n","l = len(ratings)\n","\n","tf.random.set_seed(seed)\n","shuffled = ratings.shuffle(l, seed=seed, reshuffle_each_iteration=False)\n","\n","#Save 75% of the data for training and 25% for testing:\n","train_ = int(0.75 * l)\n","test_ = int(0.25 * l)\n","\n","train = shuffled.take(train_)\n","test = shuffled.skip(train_).take(test_)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ixswf8SGPpSR","executionInfo":{"status":"ok","timestamp":1636019532262,"user_tz":-330,"elapsed":90606,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"e46133c9-0945-4290-eb13-73370293b08d"},"source":["# Now, let's find out how many uniques users/movies:\n","movie_titles = movies.batch(l)\n","user_ids = ratings.batch(l).map(lambda x: x[\"user_id\"])\n","\n","#Movies uniques:\n","unique_movie_titles = np.unique(np.concatenate(list(movie_titles)))\n","\n","#users unique\n","unique_user_ids = np.unique(np.concatenate(list(user_ids)))\n","\n","# take a look at the movies:\n","unique_movie_titles[:10]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([b'$1,000,000 Duck (1971)', b\"'Night Mother (1986)\",\n","       b\"'Til There Was You (1997)\", b\"'burbs, The (1989)\",\n","       b'...And Justice for All (1979)', b'1-900 (1994)',\n","       b'10 Things I Hate About You (1999)', b'101 Dalmatians (1961)',\n","       b'101 Dalmatians (1996)', b'12 Angry Men (1957)'], dtype=object)"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pD_X4XH8Pqy3","executionInfo":{"status":"ok","timestamp":1636019532264,"user_tz":-330,"elapsed":61,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"20389e8d-6a74-4b6d-ebd0-8c1f0dc9df1d"},"source":["#Movies uniques\n","len_films = len(unique_movie_titles)\n","print(len_films) "],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["3883\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Avhdjoq5PsFM","executionInfo":{"status":"ok","timestamp":1636019532266,"user_tz":-330,"elapsed":51,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"33c952c0-8cca-4126-ca9f-31670687cd92"},"source":["#users unique\n","len_users = len(unique_user_ids)\n","print(len_users) "],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["6040\n"]}]},{"cell_type":"markdown","metadata":{"id":"qkLbhNsgPtM8"},"source":["## Implementing a Retrieval Model\n","\n","Choosing the architecture of our model is a key part of modelling.\n","\n","Because we are building a two-tower retrieval model, we can build each tower separately and then combine them in the final model."]},{"cell_type":"markdown","metadata":{"id":"IC9RB-ARQcrn"},"source":["### The query tower\n","\n","Let's start with the query tower.\n","\n","The first step is to decide on the dimensionality of the query and candidate representations:"]},{"cell_type":"code","metadata":{"id":"fXHCBU0-QYmg"},"source":["#Higher values will correspond to models that may be more accurate, but will also be slower to fit and more prone to overfitting:\n","embedding_dimension = 32\n","\n","#We define the embedding on the user side, we must transform the user ids into a vector representation:\n","#we're going to use Keras preprocessing layers to first convert user ids to integers, and then convert those to user embeddings via an Embedding layer:\n","user_model = tf.keras.Sequential([\n","                                  tf.keras.layers.experimental.preprocessing.StringLookup(\n","                                  #User_ids vocabulary: list of unique integers that represents each user_id\n","                                  vocabulary=unique_user_ids, mask_token=None),\n","                                  # We add an additional embedding to account for unknown tokens.\n","                                  tf.keras.layers.Embedding(len(unique_user_ids) + 1, \n","                                  embedding_dimension)#embedding layer with a vector size of 32\n","                                ])"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"SpU9JdhZQiLo"},"source":["So, basically we want an embedding layer for however many user ids we have where each one is represented by a vector of 32 floating point values that  basically represents in that 64-dimensional space how similar users "]},{"cell_type":"markdown","metadata":{"id":"3cM3WtUVQfRv"},"source":["### The candidate tower"]},{"cell_type":"code","metadata":{"id":"i3C1Or8LQmPq"},"source":["# We now define the embedding of the movie portion \n","movie_model = tf.keras.Sequential([\n","                                  tf.keras.layers.experimental.preprocessing.StringLookup(\n","                                  vocabulary=unique_movie_titles, mask_token=None),\n","                                  tf.keras.layers.Embedding(len(unique_movie_titles) + 1,\n","                                  embedding_dimension) #embedding layer with a vector size of 32\n","                                 ])"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FuJD-hJ9QpG8"},"source":["### Metrics\n","\n","In our training data we have positive (user, movie) pairs. To figure out how good our model is, we need to compare the affinity score that the model calculates for this pair to the scores of all the other possible candidates: if the score for the positive pair is higher than for all other candidates, our model is highly accurate.\n","\n","To do this, we can use the tfrs.metrics.FactorizedTopK metric. The metric has one required argument: the dataset of candidates that are used as implicit negatives for evaluation.\n","\n","In our case, that's the movies dataset, converted into embeddings via our movie model:"]},{"cell_type":"code","metadata":{"id":"UnWXonnIQrTV"},"source":["#We define the desired metrics : FactorizedTopK\n","metrics = tfrs.metrics.FactorizedTopK(\n","                                     candidates=movies.batch(128).map(movie_model)\n","                                     )\n","#The Retrieval task is defined according to the FactorizedTopK metrics:\n","task = tfrs.tasks.Retrieval(\n","                            metrics=metrics\n","                           )"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"lXOv5ZSHQtb_"},"source":["### The Full Model\n","\n","We can now put it all together into a model. TFRS exposes a base model class (tfrs.models.Model) which streamlines building models: all we need to do is to set up the components in the init method, and implement the compute_loss method, taking in the raw features and returning a loss value.\n","\n","The base model will then take care of creating the appropriate training loop to fit our model."]},{"cell_type":"code","metadata":{"id":"UMExxYDiQvoK"},"source":["class MovielensModel(tfrs.Model):\n","    \n","    def __init__(self, user_model, movie_model):\n","        super().__init__()\n","        #The Two Towers: Movie and user Models:\n","        self.movie_model: tf.keras.Model = movie_model\n","        self.user_model: tf.keras.Model = user_model\n","        self.task: tf.keras.layers.Layer = task\n","\n","    def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -> tf.Tensor:\n","        # We pick out the user features and pass them into the user model.\n","        user_embeddings = self.user_model(features[\"user_id\"])\n","        # And pick out the movie features and pass them into the movie model,\n","        # getting embeddings back.\n","        positive_movie_embeddings = self.movie_model(features[\"movie_title\"])\n","\n","        # The task computes the loss and the metrics.\n","        return self.task(user_embeddings, positive_movie_embeddings)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"sVQjc_dmQxfB"},"source":["The tfrs.Model base class is a simply convenience class: it allows us to compute both training and test losses using the same method.\n","\n","Under the hood, it's still a plain Keras model. You could achieve the same functionality by inheriting from tf.keras.Model and overriding the train_step and test_step functions (see the guide for details):"]},{"cell_type":"code","metadata":{"id":"bCYXQFiaQylA"},"source":["class NoBaseClassMovielensModel(tf.keras.Model):\n","\n","    def __init__(self, user_model, movie_model):\n","        super().__init__()\n","        self.movie_model: tf.keras.Model = movie_model\n","        self.user_model: tf.keras.Model = user_model\n","        self.task: tf.keras.layers.Layer = task\n","\n","    def train_step(self, features: Dict[Text, tf.Tensor]) -> tf.Tensor:\n","\n","        # Set up a gradient tape to record gradients.\n","        with tf.GradientTape() as tape:\n","\n","            # Loss computation.\n","            user_embeddings = self.user_model(features[\"user_id\"])\n","            positive_movie_embeddings = self.movie_model(features[\"movie_title\"])\n","            loss = self.task(user_embeddings, positive_movie_embeddings)\n","\n","            # Handle regularization losses as well.\n","            regularization_loss = sum(self.losses)\n","\n","            total_loss = loss + regularization_loss\n","\n","        gradients = tape.gradient(total_loss, self.trainable_variables)\n","        self.optimizer.apply_gradients(zip(gradients, self.trainable_variables))\n","\n","        metrics = {metric.name: metric.result() for metric in self.metrics}\n","        metrics[\"loss\"] = loss\n","        metrics[\"regularization_loss\"] = regularization_loss\n","        metrics[\"total_loss\"] = total_loss\n","\n","        return metrics\n","\n","    def test_step(self, features: Dict[Text, tf.Tensor]) -> tf.Tensor:\n","\n","        # Loss computation.\n","        user_embeddings = self.user_model(features[\"user_id\"])\n","        positive_movie_embeddings = self.movie_model(features[\"movie_title\"])\n","        loss = self.task(user_embeddings, positive_movie_embeddings)\n","\n","        # Handle regularization losses as well.\n","        regularization_loss = sum(self.losses)\n","\n","        total_loss = loss + regularization_loss\n","\n","        metrics = {metric.name: metric.result() for metric in self.metrics}\n","        metrics[\"loss\"] = loss\n","        metrics[\"regularization_loss\"] = regularization_loss\n","        metrics[\"total_loss\"] = total_loss\n","\n","        return metrics"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ssFiVAo9QQ-H"},"source":["### Fitting and evaluating\n","After defining the model, we can use standard Keras fitting and evaluation routines to fit and evaluate the model.\n","\n","Adagrad is an algorithm for gradient-based optimization that does just this: It adapts the learning rate to the parameters, performing smaller updates (i.e. low learning rates) for parameters associated with frequently occurring features, and larger updates (i.e. high learning rates) for parameters associated with infrequent features. For this reason, it is well-suited for dealing with sparse data."]},{"cell_type":"code","metadata":{"id":"1qdMav8uQOTs"},"source":["#Let's first instantiate the model.\n","model = MovielensModel(user_model, movie_model)\n","\n","model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.1)) #defauly learning_rate=0.001"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"5yPHHWPqQM6b"},"source":["# Then shuffle, batch, and cache the training and evaluation data:\n","# Segment the batches so that the model runs 13 training batches (2^13) and 11 test batches (2^11) per epoch, \n","# while having a batch size which is a multiple of 2^n.\n","cached_train = train.shuffle(l).batch(8192).cache()\n","cached_test = test.batch(2048).cache()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pATY5TMuQLqD","executionInfo":{"status":"ok","timestamp":1636021194232,"user_tz":-330,"elapsed":1662006,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"258f6411-cd01-4bff-fdaf-3d7947291305"},"source":["# Then, let's train the model:\n","history_train = model.fit(cached_train, validation_data = cached_test, epochs=5)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/5\n","92/92 [==============================] - 469s 4s/step - factorized_top_k/top_1_categorical_accuracy: 2.2795e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0060 - factorized_top_k/top_10_categorical_accuracy: 0.0139 - factorized_top_k/top_50_categorical_accuracy: 0.0706 - factorized_top_k/top_100_categorical_accuracy: 0.1301 - loss: 71263.6232 - regularization_loss: 0.0000e+00 - total_loss: 71263.6232 - val_factorized_top_k/top_1_categorical_accuracy: 0.0021 - val_factorized_top_k/top_5_categorical_accuracy: 0.0140 - val_factorized_top_k/top_10_categorical_accuracy: 0.0274 - val_factorized_top_k/top_50_categorical_accuracy: 0.1126 - val_factorized_top_k/top_100_categorical_accuracy: 0.1898 - val_loss: 934.0494 - val_regularization_loss: 0.0000e+00 - val_total_loss: 934.0494\n","Epoch 2/5\n","92/92 [==============================] - 278s 3s/step - factorized_top_k/top_1_categorical_accuracy: 3.9992e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0116 - factorized_top_k/top_10_categorical_accuracy: 0.0245 - factorized_top_k/top_50_categorical_accuracy: 0.1079 - factorized_top_k/top_100_categorical_accuracy: 0.1859 - loss: 68988.6438 - regularization_loss: 0.0000e+00 - total_loss: 68988.6438 - val_factorized_top_k/top_1_categorical_accuracy: 0.0026 - val_factorized_top_k/top_5_categorical_accuracy: 0.0157 - val_factorized_top_k/top_10_categorical_accuracy: 0.0307 - val_factorized_top_k/top_50_categorical_accuracy: 0.1198 - val_factorized_top_k/top_100_categorical_accuracy: 0.1989 - val_loss: 912.1451 - val_regularization_loss: 0.0000e+00 - val_total_loss: 912.1451\n","Epoch 3/5\n","92/92 [==============================] - 278s 3s/step - factorized_top_k/top_1_categorical_accuracy: 3.3460e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0134 - factorized_top_k/top_10_categorical_accuracy: 0.0280 - factorized_top_k/top_50_categorical_accuracy: 0.1167 - factorized_top_k/top_100_categorical_accuracy: 0.1978 - loss: 68258.8027 - regularization_loss: 0.0000e+00 - total_loss: 68258.8027 - val_factorized_top_k/top_1_categorical_accuracy: 0.0027 - val_factorized_top_k/top_5_categorical_accuracy: 0.0165 - val_factorized_top_k/top_10_categorical_accuracy: 0.0316 - val_factorized_top_k/top_50_categorical_accuracy: 0.1217 - val_factorized_top_k/top_100_categorical_accuracy: 0.2020 - val_loss: 902.4336 - val_regularization_loss: 0.0000e+00 - val_total_loss: 902.4336\n","Epoch 4/5\n","92/92 [==============================] - 280s 3s/step - factorized_top_k/top_1_categorical_accuracy: 2.8927e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0144 - factorized_top_k/top_10_categorical_accuracy: 0.0298 - factorized_top_k/top_50_categorical_accuracy: 0.1218 - factorized_top_k/top_100_categorical_accuracy: 0.2044 - loss: 67840.2904 - regularization_loss: 0.0000e+00 - total_loss: 67840.2904 - val_factorized_top_k/top_1_categorical_accuracy: 0.0028 - val_factorized_top_k/top_5_categorical_accuracy: 0.0169 - val_factorized_top_k/top_10_categorical_accuracy: 0.0317 - val_factorized_top_k/top_50_categorical_accuracy: 0.1229 - val_factorized_top_k/top_100_categorical_accuracy: 0.2043 - val_loss: 896.2278 - val_regularization_loss: 0.0000e+00 - val_total_loss: 896.2278\n","Epoch 5/5\n","92/92 [==============================] - 277s 3s/step - factorized_top_k/top_1_categorical_accuracy: 2.3995e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0149 - factorized_top_k/top_10_categorical_accuracy: 0.0309 - factorized_top_k/top_50_categorical_accuracy: 0.1248 - factorized_top_k/top_100_categorical_accuracy: 0.2090 - loss: 67554.4295 - regularization_loss: 0.0000e+00 - total_loss: 67554.4295 - val_factorized_top_k/top_1_categorical_accuracy: 0.0026 - val_factorized_top_k/top_5_categorical_accuracy: 0.0169 - val_factorized_top_k/top_10_categorical_accuracy: 0.0317 - val_factorized_top_k/top_50_categorical_accuracy: 0.1237 - val_factorized_top_k/top_100_categorical_accuracy: 0.2057 - val_loss: 891.8154 - val_regularization_loss: 0.0000e+00 - val_total_loss: 891.8154\n"]}]},{"cell_type":"markdown","metadata":{"id":"9mpbRzG_Q6wH"},"source":["As the model trains, the loss is falling and a set of top-k retrieval metrics is updated. These tell us whether the true positive is in the top-k retrieved items from the entire candidate set. For example, a top-5 categorical accuracy metric of 0.2 would tell us that, on average, the true positive is in the top 5 retrieved items 20% of the time."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V26excQlRA_T","executionInfo":{"status":"ok","timestamp":1636021338006,"user_tz":-330,"elapsed":142391,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"188d6f10-adcf-4199-b48c-c7b5c68edef5"},"source":["#Evaluate the model\n","model.evaluate(cached_test, return_dict=True)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["123/123 [==============================] - 83s 672ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0026 - factorized_top_k/top_5_categorical_accuracy: 0.0169 - factorized_top_k/top_10_categorical_accuracy: 0.0317 - factorized_top_k/top_50_categorical_accuracy: 0.1237 - factorized_top_k/top_100_categorical_accuracy: 0.2057 - loss: 14021.5402 - regularization_loss: 0.0000e+00 - total_loss: 14021.5402\n"]},{"output_type":"execute_result","data":{"text/plain":["{'factorized_top_k/top_100_categorical_accuracy': 0.20568521320819855,\n"," 'factorized_top_k/top_10_categorical_accuracy': 0.03171340376138687,\n"," 'factorized_top_k/top_1_categorical_accuracy': 0.002603458473458886,\n"," 'factorized_top_k/top_50_categorical_accuracy': 0.1237102672457695,\n"," 'factorized_top_k/top_5_categorical_accuracy': 0.016932478174567223,\n"," 'loss': 891.8154296875,\n"," 'regularization_loss': 0,\n"," 'total_loss': 891.8154296875}"]},"metadata":{},"execution_count":21}]},{"cell_type":"markdown","metadata":{"id":"UbQcs6agQ7DC"},"source":["### Visualization: Total loss and Accuracy over epochs"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":400},"id":"icvrXIJHQ9IR","executionInfo":{"status":"ok","timestamp":1636021338008,"user_tz":-330,"elapsed":35,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"baba63a7-487b-4692-b43b-bec84883a715"},"source":["plt.subplots(figsize = (16,6))\n","plt.plot(history_train.history['total_loss'] )\n","plt.title(\"Total Loss over epochs\", fontsize=14)\n","plt.ylabel('Loss Total')\n","plt.xlabel('epochs')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1152x432 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":404},"id":"YGOKUvo1Q_Z1","executionInfo":{"status":"ok","timestamp":1636021339128,"user_tz":-330,"elapsed":1131,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"28a3d13d-1df7-431f-dc6b-b468fe08d0ca"},"source":["plt.subplots(figsize = (16,6))\n","plt.plot(history_train.history['factorized_top_k/top_100_categorical_accuracy'],color='green', alpha=0.8, label='Train' )\n","plt.plot(history_train.history['val_factorized_top_k/top_100_categorical_accuracy'],color='red', alpha=0.8, label='Test' )\n","plt.title(\"Accuracy over epochs\", fontsize=14)\n","plt.ylabel('Accuracy')\n","plt.xlabel('epochs')\n","plt.legend(loc='upper left')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1152x432 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"W7lE7pzqRJX7"},"source":["Test set performance is much worse than training performance. This is due to two factors:\n","\n","- Our model is likely to perform better on the data that it has seen, simply because it can memorize it. This overfitting phenomenon is especially strong when models have many parameters. It can be mediated by model regularization and use of user and movie features that help the model generalize better to unseen data.\n","- The model is re-recommending some of users' already watched movies. These known-positive watches can crowd out test movies out of top K recommendations.\n","\n","The second phenomenon can be tackled by excluding previously seen movies from test recommendations. This approach is relatively common in the recommender systems literature, but we don't follow it in these tutorials. If not recommending past watches is important, we should expect appropriately specified models to learn this behaviour automatically from past user history and contextual information. Additionally, it is often appropriate to recommend the same item multiple times (say, an evergreen TV series or a regularly purchased item)."]},{"cell_type":"markdown","metadata":{"id":"qBJQD5E2ROYF"},"source":["## Making predictions\n","\n","Now that we have a model, we would like to be able to make predictions. We can use the tfrs.layers.factorized_top_k.BruteForce layer to do this."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SQnYRG-wfNhT","executionInfo":{"status":"ok","timestamp":1636021660610,"user_tz":-330,"elapsed":599,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"1f95dd6b-3df3-479a-84d7-ca4bf0dcae45"},"source":["# Features of all the available movies.\n","movies = tfds.load(\"movielens/1m-movies\", split=\"train\", shuffle_files=True)\n","\n","#The movies dataset contains the movie id, movie title, and data on what genres it belongs to. Note that the genres are encoded with integer labels:\n","#View the data from movies dataset:\n","for x in movies.take(1).as_numpy_iterator():\n","    pprint.pprint(x)\n","\n","movies = movies.map(lambda x: x[\"movie_title\"])"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'movie_genres': array([7]),\n"," 'movie_id': b'1486',\n"," 'movie_title': b'Quiet Room, The (1996)'}\n"]}]},{"cell_type":"code","metadata":{"id":"zSNQGImsRfob"},"source":["# Recommend the 5 best movies for user 42:\n","\n","# Create a model that takes in raw query features, and\n","index = tfrs.layers.factorized_top_k.BruteForce(model.user_model)\n","\n","# recommends movies out of the entire movies dataset.\n","index.index(movies.batch(100).map(model.movie_model), movies)\n","\n","# Get recommendations.\n","_, titles = index(tf.constant([\"42\"]))\n","print(f\"Recommendations for user 42: {titles[0, :5]}\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"9LtDRNoDRiTd"},"source":["## Model serving\n","\n","After the model is trained, we need a way to deploy it.\n","\n","In a two-tower retrieval model, serving has two components:\n","\n","- a serving query model, taking in features of the query and transforming them into a query embedding, and\n","- a serving candidate model. This most often takes the form of an approximate nearest neighbours (ANN) index which allows fast approximate lookup of candidates in response to a query produced by the query model.\n","\n","In TFRS, both components can be packaged into a single exportable model, giving us a model that takes the raw user id and returns the titles of top movies for that user. This is done via exporting the model to a SavedModel format, which makes it possible to serve using TensorFlow Serving.\n","\n","To deploy a model like this, we simply export the BruteForce layer we created above:"]},{"cell_type":"code","metadata":{"id":"kre0DlQXRoh0"},"source":["# Export the query model.\n","with tempfile.TemporaryDirectory() as tmp:\n","    path = os.path.join(tmp, \"model\")\n","\n","  # Save the index.\n","    index.save(path)\n","\n","  # Load it back; can also be done in TensorFlow Serving.\n","    loaded = tf.keras.models.load_model(path)\n","\n","  # Pass a user id in, get top predicted movie titles back.\n","    scores, titles = loaded([\"42\"])\n","    \n","    print(f\"Recommendations: {titles[0][:5]}\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"5dRxk087RqnA"},"source":["from IPython.core.display import display, HTML\n","display(HTML(\"<style>.container { width:75% !important; }</style>\"))"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Oj_-StoPSAZ2"},"source":["## Tuning Retrieval (The Two Towers Model)"]},{"cell_type":"markdown","metadata":{"id":"cHwNQJLNSBFD"},"source":["### Increase embedding_dimension from 32 to 64"]},{"cell_type":"code","metadata":{"id":"kpaQDeDoSFmv"},"source":["#Higher values will correspond to models that may be more accurate, but will also be slower to fit and more prone to overfitting:\n","embedding_dimension = 64"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"pzhMU-spSGS8"},"source":["#We define the embedding on the user side, we must transform the user ids into a vector representation:\n","#we're going to use Keras preprocessing layers to first convert user ids to integers, and then convert those to user embeddings via an Embedding layer:\n","user_model = tf.keras.Sequential([\n","                                  tf.keras.layers.experimental.preprocessing.StringLookup(\n","                                  #User_ids vocabulary: list of unique integers that represents each user_id\n","                                  vocabulary=unique_user_ids, mask_token=None),\n","                                  # We add an additional embedding to account for unknown tokens.\n","                                  tf.keras.layers.Embedding(len(unique_user_ids) + 1, \n","                                  embedding_dimension)#embedding layer with a vector size of 64\n","                                ])"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7Sm0n0fpSKFB"},"source":["So, basically we want an embedding layer for however many user ids we have where each one is represented by a vector of 64 floating point values that basically represents in that 64-dimensional space how similar users."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1V-LYjEbSMhw","executionInfo":{"status":"ok","timestamp":1636022304061,"user_tz":-330,"elapsed":603298,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"11e74eef-e09d-47f1-f6b3-613cfdbc9dc4"},"source":["# We now define the embedding of the movie portion \n","movie_model = tf.keras.Sequential([\n","                                  tf.keras.layers.experimental.preprocessing.StringLookup(\n","                                  vocabulary=unique_movie_titles, mask_token=None),\n","                                  tf.keras.layers.Embedding(len(unique_movie_titles) + 1,\n","                                  embedding_dimension) #embedding layer with a vector size of 64\n","                                 ])\n","\n","#We define the desired metrics : FactorizedTopK\n","metrics = tfrs.metrics.FactorizedTopK(\n","                                     candidates=movies.batch(128).map(movie_model)\n","                                     )\n","\n","#The Retrieval task is defined according to the FactorizedTopK metrics:\n","task = tfrs.tasks.Retrieval(\n","                            metrics=metrics\n","                           )\n","\n","#Let's first instantiate the model.\n","model_1 = MovielensModel(user_model, movie_model)\n","\n","model_1.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.1)) # learning_rate default=0.001\n","\n","# Then, let's train the model:\n","history_train_1 = model_1.fit(cached_train, validation_data = cached_test, epochs=2)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/2\n","92/92 [==============================] - 284s 3s/step - factorized_top_k/top_1_categorical_accuracy: 1.5064e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0052 - factorized_top_k/top_10_categorical_accuracy: 0.0123 - factorized_top_k/top_50_categorical_accuracy: 0.0677 - factorized_top_k/top_100_categorical_accuracy: 0.1271 - loss: 71041.4379 - regularization_loss: 0.0000e+00 - total_loss: 71041.4379 - val_factorized_top_k/top_1_categorical_accuracy: 0.0023 - val_factorized_top_k/top_5_categorical_accuracy: 0.0157 - val_factorized_top_k/top_10_categorical_accuracy: 0.0298 - val_factorized_top_k/top_50_categorical_accuracy: 0.1181 - val_factorized_top_k/top_100_categorical_accuracy: 0.1974 - val_loss: 929.6202 - val_regularization_loss: 0.0000e+00 - val_total_loss: 929.6202\n","Epoch 2/2\n","92/92 [==============================] - 280s 3s/step - factorized_top_k/top_1_categorical_accuracy: 2.6928e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0121 - factorized_top_k/top_10_categorical_accuracy: 0.0257 - factorized_top_k/top_50_categorical_accuracy: 0.1128 - factorized_top_k/top_100_categorical_accuracy: 0.1928 - loss: 68552.3038 - regularization_loss: 0.0000e+00 - total_loss: 68552.3038 - val_factorized_top_k/top_1_categorical_accuracy: 0.0028 - val_factorized_top_k/top_5_categorical_accuracy: 0.0180 - val_factorized_top_k/top_10_categorical_accuracy: 0.0342 - val_factorized_top_k/top_50_categorical_accuracy: 0.1297 - val_factorized_top_k/top_100_categorical_accuracy: 0.2131 - val_loss: 903.2338 - val_regularization_loss: 0.0000e+00 - val_total_loss: 903.2338\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"biMDmrcpSXIF","executionInfo":{"status":"ok","timestamp":1636022384672,"user_tz":-330,"elapsed":80634,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"23c0c31d-55da-4069-bfd1-5f940973780e"},"source":["#Evaluate the Base model\n","model_1.evaluate(cached_test, return_dict=True)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["123/123 [==============================] - 81s 656ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0028 - factorized_top_k/top_5_categorical_accuracy: 0.0180 - factorized_top_k/top_10_categorical_accuracy: 0.0342 - factorized_top_k/top_50_categorical_accuracy: 0.1297 - factorized_top_k/top_100_categorical_accuracy: 0.2131 - loss: 14072.2262 - regularization_loss: 0.0000e+00 - total_loss: 14072.2262\n"]},{"output_type":"execute_result","data":{"text/plain":["{'factorized_top_k/top_100_categorical_accuracy': 0.21307967603206635,\n"," 'factorized_top_k/top_10_categorical_accuracy': 0.03419288620352745,\n"," 'factorized_top_k/top_1_categorical_accuracy': 0.0027674243319779634,\n"," 'factorized_top_k/top_50_categorical_accuracy': 0.12972502410411835,\n"," 'factorized_top_k/top_5_categorical_accuracy': 0.017956264317035675,\n"," 'loss': 903.2338256835938,\n"," 'regularization_loss': 0,\n"," 'total_loss': 903.2338256835938}"]},"metadata":{},"execution_count":31}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":400},"id":"29fjszAPSUBR","executionInfo":{"status":"ok","timestamp":1636022386401,"user_tz":-330,"elapsed":1746,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"93dc619a-2075-4e6e-96f7-d8a81ad1af85"},"source":["plt.subplots(figsize = (16,6))\n","plt.plot(history_train_1.history['total_loss'] )\n","plt.title(\"Total Loss over epochs\", fontsize=14)\n","plt.ylabel('Loss Total')\n","plt.xlabel('epochs')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1152x432 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":404},"id":"OxLFe6qwSVfo","executionInfo":{"status":"ok","timestamp":1636022386402,"user_tz":-330,"elapsed":29,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"8dc1614f-a18d-46c9-9f64-100ca3257777"},"source":["plt.subplots(figsize = (16,6))\n","plt.plot(history_train_1.history['factorized_top_k/top_100_categorical_accuracy'],color='green', alpha=0.8, label='Train' )\n","plt.plot(history_train_1.history['val_factorized_top_k/top_100_categorical_accuracy'],color='red', alpha=0.8, label='Test' )\n","plt.title(\"Accuracy over epochs\", fontsize=14)\n","plt.ylabel('Accuracy')\n","plt.xlabel('epochs')\n","plt.legend(loc='upper left')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1152x432 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"vVFj4ie4Sagg"},"source":["### Reduce Adagrad learning_rate\n","\n","learning_rate = 0.01"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nanoYLeXScs1","executionInfo":{"status":"ok","timestamp":1636022987280,"user_tz":-330,"elapsed":600904,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"eb56c9ad-3f5d-4f3f-991e-383d8c7ad128"},"source":["#Let's first instantiate the model.\n","model_2 = MovielensModel(user_model, movie_model)\n","\n","model_2.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.01)) # learning_rate default=0.001\n","\n","# Then, let's train the model:\n","history_train_2 = model_2.fit(cached_train, validation_data = cached_test, epochs=2)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/2\n","92/92 [==============================] - 279s 3s/step - factorized_top_k/top_1_categorical_accuracy: 2.3195e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0182 - factorized_top_k/top_10_categorical_accuracy: 0.0369 - factorized_top_k/top_50_categorical_accuracy: 0.1406 - factorized_top_k/top_100_categorical_accuracy: 0.2284 - loss: 67388.2888 - regularization_loss: 0.0000e+00 - total_loss: 67388.2888 - val_factorized_top_k/top_1_categorical_accuracy: 0.0029 - val_factorized_top_k/top_5_categorical_accuracy: 0.0186 - val_factorized_top_k/top_10_categorical_accuracy: 0.0356 - val_factorized_top_k/top_50_categorical_accuracy: 0.1335 - val_factorized_top_k/top_100_categorical_accuracy: 0.2187 - val_loss: 898.6080 - val_regularization_loss: 0.0000e+00 - val_total_loss: 898.6080\n","Epoch 2/2\n","92/92 [==============================] - 279s 3s/step - factorized_top_k/top_1_categorical_accuracy: 2.2795e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0196 - factorized_top_k/top_10_categorical_accuracy: 0.0394 - factorized_top_k/top_50_categorical_accuracy: 0.1470 - factorized_top_k/top_100_categorical_accuracy: 0.2367 - loss: 67149.8164 - regularization_loss: 0.0000e+00 - total_loss: 67149.8164 - val_factorized_top_k/top_1_categorical_accuracy: 0.0028 - val_factorized_top_k/top_5_categorical_accuracy: 0.0190 - val_factorized_top_k/top_10_categorical_accuracy: 0.0360 - val_factorized_top_k/top_50_categorical_accuracy: 0.1349 - val_factorized_top_k/top_100_categorical_accuracy: 0.2206 - val_loss: 896.7574 - val_regularization_loss: 0.0000e+00 - val_total_loss: 896.7574\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5vfZOV7oSjne","executionInfo":{"status":"ok","timestamp":1636023069178,"user_tz":-330,"elapsed":81923,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"eede7966-0d05-4b8c-a42a-c92e7c2c3857"},"source":["#Evaluate the Base model\n","model_2.evaluate(cached_test, return_dict=True)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["123/123 [==============================] - 80s 651ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0028 - factorized_top_k/top_5_categorical_accuracy: 0.0190 - factorized_top_k/top_10_categorical_accuracy: 0.0360 - factorized_top_k/top_50_categorical_accuracy: 0.1349 - factorized_top_k/top_100_categorical_accuracy: 0.2206 - loss: 14006.8701 - regularization_loss: 0.0000e+00 - total_loss: 14006.8701\n"]},{"output_type":"execute_result","data":{"text/plain":["{'factorized_top_k/top_100_categorical_accuracy': 0.2205861210823059,\n"," 'factorized_top_k/top_10_categorical_accuracy': 0.036028504371643066,\n"," 'factorized_top_k/top_1_categorical_accuracy': 0.002843408612534404,\n"," 'factorized_top_k/top_50_categorical_accuracy': 0.13492393493652344,\n"," 'factorized_top_k/top_5_categorical_accuracy': 0.018952058628201485,\n"," 'loss': 896.7573852539062,\n"," 'regularization_loss': 0,\n"," 'total_loss': 896.7573852539062}"]},"metadata":{},"execution_count":35}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":400},"id":"l0rdKNWkSgp_","executionInfo":{"status":"ok","timestamp":1636023069179,"user_tz":-330,"elapsed":10,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"fb3cb3f6-d4ec-42db-8dfe-2a3a2e11741a"},"source":["plt.subplots(figsize = (16,6))\n","plt.plot(history_train_2.history['total_loss'] )\n","plt.title(\"Total Loss over epochs\", fontsize=14)\n","plt.ylabel('Loss Total')\n","plt.xlabel('epochs')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1152x432 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":399},"id":"U31d62SJSgmR","executionInfo":{"status":"ok","timestamp":1636023069992,"user_tz":-330,"elapsed":819,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"f6d6c793-5f59-4792-cce0-006b6ce518aa"},"source":["plt.subplots(figsize = (16,6))\n","plt.plot(history_train_2.history['factorized_top_k/top_100_categorical_accuracy'],color='green', alpha=0.8, label='Train' )\n","plt.plot(history_train_2.history['val_factorized_top_k/top_100_categorical_accuracy'],color='red', alpha=0.8, label='Test' )\n","plt.title(\"Accuracy over epochs\", fontsize=14)\n","plt.ylabel('Accuracy')\n","plt.xlabel('epochs')\n","plt.legend(loc='upper left')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA8YAAAGHCAYAAAByCkshAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXxU5b0/8M/sSWbOJJmZkHXOjMSAIFskQACBkkRKVe7lttxra3GpOyoopYpQfpZWuY0l4K1WRSlyL6It2mrr0t4icFkEbaFKeVUQDSELSSArzGSZJDPn+f0x5IQxgFHJLMnn/Xr5ambmmTPfZ3yK+fAsRyOEECAiIiIiIiIapLSRLoCIiIiIiIgokhiMiYiIiIiIaFBjMCYiIiIiIqJBjcGYiIiIiIiIBjUGYyIiIiIiIhrUGIyJiIiIiIhoUGMwJiIiokGjvLwcGo0GBw4ciHQpREQURRiMiYhowPnwww+h0+kwderUSJdCREREMYDBmIiIBpxf//rXuPfee/HPf/4TR44ciXQ56OrqinQJYdXZ2RnpEoiIiL4UBmMiIhpQ2tvb8corr+Cuu+7CvHnzsGHDhl5tPvjgAxQUFMBsNiMxMREFBQWoqakBAAghsGbNGuTk5MBkMiErKwvLli0DcOFluBqNBr/73e9C2vzmN79BQUEB4uPj8fzzz6OxsRHf+973kJWVhfj4eFx55ZXYuHFjyHUu9tkFBQW4//77Q9p7PB4kJCTg9ddfv+D38frrr2P06NEwmUxwOp1YtWoVhBAAgOXLl2P8+PG93jNlyhQsWrRIfbxx40aMHDkScXFxGDZsGJ588kkoihLS/2eeeQbf/va3YTabsXz58vPWIoTAL37xC2RnZyM+Ph6jR4/G5s2b1de7v7tXXnkFV199NeLi4nDFFVdg69atIdfZvXs3Jk2ahLi4OKSmpmLx4sUhYfxi32O3iooKXHPNNUhISMDIkSPx7rvvqq91dXVh0aJFyMjIUL+3Rx555ILfMRERDQCCiIhoANm0aZMYM2aMEEKI//u//xMpKSmis7NTff3gwYMiLi5O3HnnneKjjz4Shw8fFuvWrRMVFRVCCCEeeeQRkZiYKDZs2CA+++wzsW/fPvHMM88IIYQ4fvy4ACD2798f8pkAxGuvvRbSxuVyiddee02UlZWJqqoqceLECfGLX/xCfPTRR+LYsWPi+eefFwaDQWzbtk29zsU++5VXXhHJycnC5/Op7detW9erf+c6cOCA0Gq14tFHHxVHjx4VmzdvFmazWTz11FNCCCE+/vhjAUAcOXJEfc+xY8cEAPHXv/5VCCHECy+8INLS0tS+vPnmmyI1NVU8/fTTIf1PSUkR69evF8eOHRNlZWXnrWf58uVi2LBh4s9//rMoKysTL7/8skhISBBvv/12yHeXmZkptmzZIo4cOSLuv/9+ERcXJ06cOCGEEOLEiRMiISFB3H333eLw4cPirbfeEqmpqeKHP/xhn77H7s8YPny4ePPNN8Wnn34qbr75ZmGz2YTX6xVCCFFSUiKysrLErl27REVFhdi7d6948cUXz9snIiIaGBiMiYhoQJkxY4ZYvXq1EEIIRVHUgNrtxhtvFPn5+ed9r9frFSaTSTz33HPnff3LBOOSkpIvrPWGG24Qt99+e58+2+fzCbvdLn7zm9+oz02cOFEsWbLkgte/8cYbxcyZM0Oe+8lPfiIyMzPVx7m5uWLFihXq48cee0wMGzZMfex0OsWmTZtCrvHkk0+KESNGqI8BiPvvv/9iXRUtLS0iLi5O7N69O+T5Bx54QHzrW98SQvR8d48//rj6eiAQEDk5OeLHP/6xECIYri+//HIRCATUNhs3bhRGo1G0trb2+d/hunXr1OdOnDghAIg9e/YIIYRYuHChKCgoEIqiXLRPREQ0cHApNRERDRilpaV47733cOONNwIILvH9/ve/H7Kc+qOPPkJBQcF533/48GF0dHSgsLDwa9eSl5cX8jgQCGDVqlUYM2YM7HY7LBYLXn/9dVRWVvbps00mE2666Sa8+OKLAICPP/4Yf/vb33D77bdfsIYjR470OoDs6quvRnV1NTweDwBg/vz5eOWVV9TXX375ZXz/+98HANTX16Oqqgp33303LBaL+s8jjzyCY8eOXbS/n3f48GH4fD7Mnj075FrPPfdcr2tNnjxZ/Vmr1WLSpEk4fPiw2qf8/HxotT2/wlx99dXo7OxEaWlpn/8djhkzRv05IyMDAFBXVwcAuPXWW3Hw4EEMGzYM9913H955552QpeNERDTw6CNdABER0aXy61//GoFAALIsq8+Js/tpq6qq4HQ6v9b1u8NY9zWBCx+sZTabQx6XlJRgzZo1+OUvf4nRo0fDYrFg+fLlahjrizvuuANjxoxBZWUlXnzxRUyePBkjRoz4Cj0J/qUBAHzve9/Dww8/jPfffx8mkwmffPIJ5s+fDwBqGFy3bh2mTJly0et9vr+f132tt956K+TfDwAYDIav1IfP6+5TX5z7md3v667xqquuQnl5Of7yl79g+/btuOWWWzB27Fi8++67IYGciIgGDgZjIiIaEPx+P/7nf/4HP//5z3H99deHvHbTTTdh48aNePTRR5Gbm4sdO3ac9xojRoyAyWTC9u3bkZOT0+v1lJQUAEBtba363MGDB/tU33vvvYc5c+bgpptuAhAM159++imSkpL69NkAcOWVV2LSpElYv349Nm/ejFWrVl30M0eMGIG9e/f2qiMrKwuSJAEA0tPTUVBQgJdffhkmkwmTJ0/G0KFDAQCpqanIyMjAsWPHcPPNN/epnxcycuRImEwmVFRUXHDGvlv34WhA8Hv629/+hnnz5ql9evXVV6EoihpS33vvPRiNRmRnZyMQCHzh99gXkiRh3rx5mDdvHm699Vbk5+ejtLQUw4YN+8rXJCKi6MVgTEREA8I777yDhoYG3HnnnbDb7SGvffe738W6devw//7f/8NDDz2E/Px83HXXXbjvvvsQFxeHPXv2YNasWZBlGQ888ACWLVsGk8mE6dOno7GxEX//+9+xYMECxMfHIz8/H0888QSys7Nx5syZXqcdX8iwYcOwZcsWvPfee3A4HHj66adx/Phx5ObmAggGsYt9drc777wT99xzDwwGA2644YaLfuaSJUswYcIErFy5EjfeeCP279+PNWvW4D//8z9D2s2fPx9LliyB0WjEj3/845DXfvrTn2LhwoVISkrCtddei66uLnz44Yeorq7uc9+7+/ejH/0IP/rRjyCEwPTp09HS0oIPPvgAWq0Wd911l9r2ueeew7BhwzB69Gg8++yzqKioUL+De++9F//1X/+Fe++9Fw888ADKysrwyCOP4P7770dCQgIA9Ol7vJi1a9ciPT0d48aNg8FgwCuvvAKr1YqsrKw+95eIiGJMRHc4ExERXSJz5swR11xzzXlf6z5p+S9/+YsQQog9e/aIadOmibi4OJGYmCgKCwtFTU2NECJ42NPPf/5zcdlllwmDwSCysrLE8uXL1WsdPnxYTJkyRcTHx4tRo0aJ3bt3n/fwrc8f0NXU1CT+7d/+TVgsFpGSkiIeeughsWDBAjFjxgy1zRd9thBCtLa2CovFIn7wgx/06Xv5/e9/L0aNGqVe7/HHH+91qJTX6xUJCQnCYDCIhoaGXtd45ZVXRG5urjCZTCIpKUlMnTo15BCwc/t/MYqiiKeeekqMGDFCGI1G4XA4RFFRkdi6dasQoue727x5s5g8ebIwmUxi2LBh4k9/+lPIdXbt2iUmTpwojEajGDJkiHjwwQdDTuu+2PfYlwPUXnjhBZGbmyssFouQJElMnz5d7N279wv7R0REsUsjxDkbpYiIiCiq1dTUQJZl7Nq1q9fBWrGuvLwcl112Gfbv3/+Fh3kRERFdSlxKTUREFAO6urrQ2NiI5cuXIzc3d8CFYiIiokji0YpEREQxYO/evUhPT8e+ffuwfv36SJdDREQ0oHApNREREREREQ1qYVtKffDgQWzcuBGKoqCwsBBz584Nef3tt9/G9u3bodPpYLVasWDBAqSkpKC+vh4lJSVQFAWBQACzZ8/GrFmz0N7ejkcffVR9f1NTE6ZNm4Zbb70VO3fuxEsvvQSbzQYAmD17NgoLC8PVVSIiIiIiIoohYZkxVhQFDzzwAFasWAG73Y5ly5bhgQceCLntwT//+U/k5OTAZDJh69at+Pjjj7F48WL4/X4IIWAwGODz+bBkyRI89thjaujttnTpUtxyyy0YOXIkdu7ciWPHjuH222/v764RERERERFRjAvLHuPS0lKkpaUhNTUVer0eU6ZMwf79+0PajBo1CiaTCQCQk5ODpqYmAIBer4fBYAAQPHhEUZRe16+pqYHH48GIESP6uSdEREREREQ00IRlKXVTUxPsdrv62G6347PPPrtg+x07dmDcuHHq44aGBhQXF+PkyZOYP39+r9niffv2YfLkydBoNOpzf/3rX3HkyBGkp6fjlltugcPh6PU527Ztw7Zt2wAAxcXF6Ozs/Mp9DAe9Xg+/3x/pMmiQ4zikaMBxSNGCY5GiAcchRYNYGIdGo/GCr0Xd7Zp2796NsrIyrFy5Un3O4XCgpKQETU1NWL16NfLz85GUlKS+vnfvXixcuFB9PH78eEydOhUGgwHvvvsunnnmGfzkJz/p9VlFRUUoKipSHzc0NPRPpy4Rh8MR9TXSwMdxSNGA45CiBcciRQOOQ4oGsTAOMzIyLvhaWJZS22w2NDY2qo8bGxt7zfoCwKFDh/DGG2/g4YcfVpdPf/46TqcTn3zyifpceXk5FEXB0KFD1eckSVLfX1hYiLKyskvZHSIiIiIiIhpAwhKMs7OzUVtbi7q6Ovj9fuzbtw95eXkhbY4fP47169fj4YcfRmJiovp8Y2OjusS5paUFR48eDUn6e/fuxdSpU0Ou1dzcrP584MCBkEO+iIiIiIiIiM4VlqXUOp0Ot912G1atWgVFUTBz5kw4nU5s2bIF2dnZyMvLw+bNm+Hz+bB27VoAwan4pUuXorq6Gps2bYJGo4EQAnPmzIEsy+q133//fSxbtizk8/785z/jwIED0Ol0sFgsuPfee79S3UII+Hw+KIoSsn85Uk6dOoWOjo5Lfl0hBLRaLeLi4qKin0REREREROEUlts1xYqampqQx+3t7TAYDNDro2Mrdn9uaPf7/ejq6kJ8fHy/XJ8GjljYP0IDH8chRQuORYoGHIcUDWJhHEZ8j3GsUhQlakJxf9Pr9ee9FRYREREREdFAx2B8EYNtWfFg6y8REREREREQhbdroh5NTU244YYbAAD19fXQ6XTqad7vvPPORe/D9Y9//AO/+93v8Nhjj4WlViIiIiIioljFYBzFbDYb3n33XQDAmjVrIEkS7rrrLvV1v99/waXeY8eOxdixY8NSJxERERERUSxjMI4xDz74IEwmEz7++GPk5eXhX//1X/Hoo4+io6MDcXFxWLt2LS6//HLs27cP69atw6ZNm7BmzRpUV1ejsrIS1dXVuOOOO3D77bdHuitERERERERRgcG4j9YdWoeyM2WX9JpDE4finjH3fOn31dbW4o9//CN0Oh28Xi/eeOMN6PV67N69G0888QTWr1/f6z2lpaV47bXX0NraimnTpuHmm2+GwWC4FN0gIiIiIiKKaQzGMej666+HTqcDAHg8Hjz44IM4fvw4NBoNurq6zvuewsJCmEwmmEwmOBwO1NfXX/S4ciIiIiIioi+iCAW1rbX4uPVj5MTlwKi78DlI0YzBuI++ysxuf0lISFB/Xr16NaZMmYINGzagqqoK8+bNO+97TCaT+rNOp0MgEOj3OomIiIiIaGBQhIKTrSdR6a1EhacCFd4KVHgqUOWtQpfSBb1ej/+a9l/ITsqOdKlfCYNxjPN6vUhLSwMAvPrqqxGuhoiIiIiIYpkiFJxqO6WG30pPJSq9wX86A51qO0e8A26rG+NSxsFldWGsPBbJSnIEK/96GIxj3IIFC/Dggw/il7/8JQoLCyNdDhERERERxQBFKKhrqzvvDHBHoENt54h3QJZkXOu+Fm6rG7JVhktyIcGQEHI9h8OBhoaGcHfjktEIIUSki4gWNTU1IY/b2tpCli1Hml6vh9/v77frR1t/KTrF+h96NDBwHFK04FikaMBxSBcjhEB9e70afCs9lSj3lKPKWwVfwKe2s8XZIEsyXFYXXJILLqsLsiTDYrT06XNiYRxe7IwlzhgTERERERHFOCEEGtobUO4tV5c/V3gqUOmtRLu/XW2XbEqGy+rCN93fhCzJ6gywZJQiWH3kMRgTERERERHFCCEEGn2Naugt95QH9wB7KtHmb1PbJZmSIEsyiuQidRZYtsqwGq0RrD56MRgTERERERFFGSEEmjuag/t/z9kDXOmtRGtXq9rOarTCZXWhwFkA2SoH9wFLMhJNiRGsPvYwGBMREREREUXIuQH43OXPFZ4KtHS1qO0sBgvcVjdmZM0I2QOcHBe7J0FHEwZjIiIiIiKiMGj2NYecAt19ENbnA7DL6sK0zGnBJdBnl0EnmZKg0WgiWP3AxmBMRERERER0CZ3pONOz/9dTqS6D9nR61DZmgxmyJOPqzKvVGWCX1YVkUzIDcAQwGEexpqYm3HDDDQCA+vp66HQ62Gw2AMA777wDo9F40ffv27cPBoMBEyZM6PdaiYiIiIgGG2+nN2T/b/cy6NMdp9U2CfoEyFYZ+en56v5fl9UFe5ydATiKMBhHMZvNhnfffRcAsGbNGkiShLvuuqvP73///fdhNpsZjImIiIiIvgZvp1dd+nzuMujmjma1Tbw+HrIkY2LaRHX/r9vqZgCOEQzGMebQoUP46U9/itbWVthsNjz55JNITU3Fhg0b8NJLL0Gv1yMnJwfLly/HSy+9BJ1Oh9///vd4/PHHMWnSpEiXT0REREQUtVq7Ws97CnSTr0ltE6eLg1NyIi81L3gP4LN7gFPiUxiAYxiDcR+Z162Dvqzskl7TP3QoWu+5p8/thRBYsWIFNm7cCLvdjj/+8Y944oknsHbtWjzzzDN4//33YTKZcObMGSQmJuKmm26C2WzGPV/iM4iIiIiIBrq2rraQw68qvZWo9Faiob1BbWPSmeCUnMgdkhtyCvSQhCHQarQRrJ76A4NxDOno6MDRo0fx3e9+FwCgKAqGDBkCABgxYgTuv/9+zJ49G7Nnz45kmUREREREUaHd367O+pZ7ylHlrUK5pzwkABt1RsiSjDGOMersr2yVkZqQygA8iDAY99GXmdntT8OGDcNbb73V6/lNmzbhgw8+wLvvvounnnoK27dvj0B1RERERETh1+5vV0OvejskTwXq2+vVNgatAU7JidGO0SEzwGnmNAZgYjCOJUajEU1NTThw4ADy8vLQ1dWFsrIy5OTkoKamBlOnTsXEiRPx5ptvorW1FWazGS0tLV98YSIiIiKiGODz+1Dlreo5BfrscuhTbafUNnqtHk7JiZH2kSGnQKeb0xmA6YIYjGOIVqvF888/j0cffRQejweBQAB33HEHhg4dioULF8Lr9UIIgdtuuw2JiYm45pprcPfdd+Mvf/kLD98iIiIiopjREegIBuCzy6C7Q/Cp1lMQEAAAnVaHLEsWhtuGY5ZrlnoKdLo5HTqtLsI9oFijEUKISBcRLWpqakIet7W1ISEhIULV9KbX6+H3+/vt+tHWX4pODocDDQ0NX9yQqB9xHFK04FikaBDL47Az0KnOAJ97ENbJ1pM9AVijQ6YlMzj7a5XVZdAZ5gwG4CgSC+MwIyPjgq9xxpiIiIiIiPpVZ6ATJ1pOBMOvtxyVnuAp0LWttVCEAiAYgDMsGchOzEaBs0ANwZmWTOi1jC3UvzjCiIiIiIjokuhSulDdUq0eftW9DLqmtUYNwFqNFhnmDLitbkzPnB48CdoaDMAGrSHCPaDBisGYiIiIiIi+FL/i7wnA5xyEVdMSGoDTzelwWV24OvNq9SCsTEsmjDpjhHtAFIrB+CIG2/brwdZfIiIiIro4v+JHTUuNGn67Z4CrW6oREAEAgAYapJvTIVtlTM2YGrwPsCTDKTkZgClmMBhfhFarhd/vh14/8L8mv98PrZbH1xMRERENRgElgNrW2p77AJ8NwidaTiCg9ATgVHMqXJIL+en56m2QnJITJp0pwj0g+noGfuL7GuLi4uDz+dDR0QGNRhPpcmAymdDR0XHJryuEgFarRVxc3CW/NhERERFFD0UoPQH47EFYVd4qVHmr4Fd67n6SmpAKl9WFCakTgnuApWAAjtPz90UamMIWjA8ePIiNGzdCURQUFhZi7ty5Ia+//fbb2L59O3Q6HaxWKxYsWICUlBTU19ejpKQEiqIgEAhg9uzZmDVrFgBg5cqVaG5uhtEYXKKxYsUKJCYmoqurC7/61a9QVlYGSZLw4IMPYsiQIV+6Zo1Gg/j4+K/f+UskFo5AJyIiIqLI6w7A6j2Azy6DrvJWoUvpUtsNSRgCl+TC+CHjQ2aA4/XR8zswUTiEJRgrioINGzZgxYoVsNvtWLZsGfLy8pCVlaW2cbvdKC4uhslkwtatW7F582YsXrwYycnJePzxx2EwGODz+bBkyRLk5eXBZrMBABYtWoTs7OyQz9uxYwfMZjOefvpp7N27Fy+//DIWL14cjq4SEREREYWNIhRUe6vxUe1H6r2Au0PwuQHYEe+A2+rGuJRx6gywbJUZgInOCkswLi0tRVpaGlJTUwEAU6ZMwf79+0OC8ahRo9Sfc3JysGfPnmCB5+zv7erqgqIoX/h5Bw4cwL//+78DAPLz8/Hiiy9CCBEVy6GJiIiIiL4sRSioa6vrmQE+uwe4yluFgCYAvz+4DNoR74AsybjOcV3wFOiz9wJOMCREuAdE0S0swbipqQl2u119bLfb8dlnn12w/Y4dOzBu3Dj1cUNDA4qLi3Hy5EnMnz9fnS0GgGeffRZarRaTJk3Cd77zHWg0mpDP0+l0SEhIgNfrhdVq7YfeERERERFdGkII1LXXBff/dh+EdTYA+wI+tZ09zg7ZKuNb7m9hVNYoJItkuKwumA3mCFZPFLui7vCt3bt3o6ysDCtXrlSfczgcKCkpQVNTE1avXo38/HwkJSVh0aJFsNlsaG9vx5o1a7B7927MmDGjz5+1bds2bNu2DQBQXFwMh8NxqbtzSen1+qivkQY+jkOKBhyHFC04FumrEkKgrq0OZc1lOH76OI6fPo6y5jKUnylHW1eb2s4Wb8PQpKHIk/MwNGkohiYNhTvJDaupZ8JHr9erM8ZEkRLrfx6GJRjbbDY0NjaqjxsbG0NmfbsdOnQIb7zxBlauXAmDwXDe6zidTnzyySfIz89XrxEfH4+rr74apaWlmDFjhvp5drsdgUAAbW1tkCSp1/WKiopQVFSkPo72g614+BZFA45DigYchxQtOBbpiwgh0OhrVPf9ds8CV3oq0ebvCcBJpiTIkowZ6TPUPcAuqwuSsffvsJ3eTjR4e8YdxyFFg1gYhxkZGRd8LSzBODs7G7W1tairq4PNZsO+ffuwaNGikDbHjx/H+vXrsXz5ciQmJqrPNzY2QpIkGI1GtLS04OjRo7j++usRCATQ2toKq9UKv9+Pv//97xg9ejQAYPz48di5cyeGDRuGDz74AFdeeSX3FxMRERFRvxFCoMnXpO797Q7Cld5KtHa1qu0STYmQJRmFcqF6CrQsyUg0JV7k6kTU38ISjHU6HW677TasWrUKiqJg5syZcDqd2LJlC7Kzs5GXl4fNmzfD5/Nh7dq1AIJ/47B06VJUV1dj06ZN0Gg0EEJgzpw5kGUZPp8Pq1atQiAQgKIoGD16tDr7W1BQgF/96ldYuHAhLBYLHnzwwXB0k4iIiIgGOCEEmjua1eDbHYIrvBUhAVgySnBb3fhG1jcgS7J6EFaSKSmC1RPRhWiEECLSRUSLmpqaSJdwUbGwPIEGPo5DigYchxQtOBYHtmZfc8gp0N0HYrV0tahtLAaLOuvrsrrUZdBJpqSwrVjkOKRoEAvjMOJLqYmIiIiIotXpjtMhp0B3h2FPp0dtYzaY4ZJcmJY5LSQEJ5uSuWWPaABgMCYiIiKiQcHT6QnZ/9sdhM90nFHbJOgT4LK6MDl9csgMsC3OxgBMNIAxGBMRERHRgOLt9KpLn7uXQVd4KnC647TaJl4fD1mSMSltkroU2m11wx5nZwAmGoQYjImIiIgoJrV0tvQEYG/PTHCTr0ltE6+Ph1NyYkLqBMhWWZ0BTolPYQAmIhWDMRERERFFtdau1tBToM+G4UZfo9rGpDNBlmRcNeSqkFOgU+JToNVoI1g9EcUCBmMiIiIiigrt/vbQ/b9nZ4Ib2ntOujXqjJAlGWNTxqqzvy6rC0MShjAAE9FXxmBMRERERGHV7m9HlbdKPfyq+0Cs+vZ6tY1Ba4BTcmK0Y7QafmVJRpo5jQGYiC45BmMiIiIi6hfdAfjzy6Dr2urUNt0B+Er7leop0LIkI92czgBMRGHDYExEREREX0tHoEMNwOeeAl3XVgcBAQDQa/XIsmThCtsVmO2aDdka3AeclpAGnVYX4R4Q0WDHYExEREREfdIZ6AwG4LOHX3UvhT7ZelINwDqtDpnmTAxPHo5rXNeoy6AzzBkMwEQUtRiMiYiIiChEdwCu9FaqIbjSW4na1looQgEA6DQ6ZFoycXnS5ShwFgRvhSS5kGnJhF7LXzGJKLbwTy0iIiKiQaoz0InqluqeU6DP7gM+NwBrNVpkWDLgtroxI2tGzwywJQMGrSHCPSAiujQYjImIiIgGuC6lCzUtNSH7fyu8FahpqQkJwOnmdLisLkzLnBa8D7AkI9OSCaPOGOEeEBH1LwZjIiIiogHCr/hR3VIdsvy5wlOB6pZqBEQAQE8AliUZUzOmwiUFT4F2Sk4GYCIatBiMiYiIiGJMQAmgprVnBrj7IKzq1moElGAA1kCDNHMaZEnG5PTJ6h7gLCkLJp0pwj0gIoouDMZEREREUUoRCmpba4P7fz2VKPcG//dEywn4Fb/aLjUhFS6rC5PSJqn3As6yZCFOHxfB6omIYgeDMREREVGEdQfg7qXPFZ4KVHorUeWtQpfSpbYbkjAELsmFvNQ8yJIMl9UFp+REvD4+gtUTEcU+BmMiIiG5gzcAACAASURBVCKiMFGEgpOtJ3sC8NmDsD4fgFPiU+CyupA7JBeyJMNtdTMAExH1IwZjIiIioktMEQrq2upCwm+lN3gYVmegU23niHfAJbkwNmWsegq0y+piACYiCjMGYyIiIqKvSAiBU22n1MOvumeCq7xV8AV8ajt7nB2yVca17mvhsrrUAGw2mCNYPRERdWMwJiIiIvoCQgjUt9eHLH0u95TjpO8kPO0etZ0tzgZZkvFN9zeDt0E6exK0xWiJYPVERPRFGIyJiIiIzhJCoNHXGDL7270Mut3frrZLMiXBZXXh2suvRYouRQ3AklGKYPVERPRVMRgTERHRoCOEQJOvSZ0BPncPcGtXq9ou0ZQIWZJRJBepy59dVhesRisAwOFwoKGhIVLdICKiS4TBmIiIiAYsIQSaO5pDwm/3gVjnBmDJKMFtdeMbWd9QT4GWrTKSTEkRrJ6IiMKFwZiIiIhinhACpztO95oBrvBUoKWrRW1nMVjgtroxI2sGXJJLPQgryZQEjUYTwR4QEVEkMRgTERFRTDndcTrkFOhKb/Bnb6dXbWM2mOGSXJiWOS3kFOhkUzIDMBER9cJgTERERFHJ0+kJmf3tDsJnOs6obRL0CXBZXZiSPkXd/+uSXLDF2RiAiYiozxiMiYiIKKK8nV513++5Qfh0x2m1Tbw+Hi6rC5PSJgX3/56dAbbH2RmAiYjoa2MwJiIiorBo6WxRw++5t0Jq7mhW28Tr4+GUnJiYNhGyJEO2ynBLbjjiHQzARETUbxiMiYiI6JJq7WrtdQJ0pacSjb5GtY1JZ4IsyRifOj7kFOgh8UMYgImIKOwYjImIiOgraetqU0PvuQdhNbT33NfXqDNClmSMGzIuuPz57EnQQxKGQKvRRrB6IiKiHgzGREREdFHt/nZUeipDlkGXe8pDArBBa4AsyRjjGAPZ2hOAUxNSGYCJiCjqhS0YHzx4EBs3boSiKCgsLMTcuXNDXn/77bexfft26HQ6WK1WLFiwACkpKaivr0dJSQkURUEgEMDs2bMxa9YsdHR0YO3atTh16hS0Wi3Gjx+P73//+wCAnTt34qWXXoLNZgMAzJ49G4WFheHqKhERUUxq97ejylvVaxl0XVud2sagNcApOTHKPko9BVqWZKSb0xmAiYgoZoUlGCuKgg0bNmDFihWw2+1YtmwZ8vLykJWVpbZxu90oLi6GyWTC1q1bsXnzZixevBjJycl4/PHHYTAY4PP5sGTJEuTl5cFsNmPOnDkYNWoU/H4/fvazn+Gjjz5Cbm4uAGDKlCm4/fbbw9E9IiKimOLz+4IB+HMHYZ1qO6W20Wv1yLJk4QrbFZjtmh08BMvqRlpCGnRaXQSrJyIiuvTCEoxLS0uRlpaG1NRUAMHQun///pBgPGrUKPXnnJwc7NmzJ1igvqfErq4uKIoCADCZTOp79Ho9LrvsMjQ29hzqQURENNh1BDrOOwN8qvUUBAQAQKfVIcuSheG24ZjlmqUehJVuTmcAJiKiQSMswbipqQl2u119bLfb8dlnn12w/Y4dOzBu3Dj1cUNDA4qLi3Hy5EnMnz9fXSLdrbW1FX//+99x7bXXqs/99a9/xZEjR5Ceno5bbrkFDofjEvaIiIgoenQGOlHlrQqG3+5ZYE8lTradhCKCf6Gs0+iQaclETlIOCp2FwWXQkgsZlgzotTxyhIiIBreo+y/h7t27UVZWhpUrV6rPORwOlJSUoKmpCatXr0Z+fj6SkpIAAIFAAL/85S/xrW99S52RHj9+PKZOnQqDwYB3330XzzzzDH7yk5/0+qxt27Zh27ZtAIDi4uKoD896vT7qa6SBj+OQosFgHYedgU5UnqnE8dPHUXa6LPi/zWWo9larAVir0cJpdWJk2khcn3w9Lku6DEOThsJpdcKgM0S4BwPPYB2LFF04DikaxPo4DEswttlsIcucGxsbe836AsChQ4fwxhtvYOXKlTAYev/H22azwel04pNPPkF+fj4A4Pnnn0daWhquu+46tZ0kSerPhYWF2Lx583nrKioqQlFRkfq4oaHhvO2ihcPhiPoaaeDjOKRoMNDHYZfSheqW6uDS53OWQde01oQE4AxzBlxWF/JT8tWDsDItmTBoP/ff0ABwpvlMBHoy8A30sUixgeOQokEsjMOMjIwLvhaWYJydnY3a2lrU1dXBZrNh3759WLRoUUib48ePY/369Vi+fDkSExPV5xsbGyFJEoxGI1paWnD06FFcf/31AIDf/va3aGtrwz333BNyrebmZiQnJwMADhw4ELKXmYiIKFr4FX8wAJ+9F3D3HuCalhoERABAMACnm9MhSzKmZk6FW3JDtsrIsmTBqDNGuAdEREQDQ1iCsU6nw2233YZVq1ZBURTMnDkTTqcTW7ZsQXZ2NvLy8rB582b4fD6sXbsWQPBvHJYuXYrq6mps2rQJGo0GQgjMmTMHsiyjsbERr7/+OjIzM7F06VIAPbdl+vOf/4wDBw5Ap9PBYrHg3nvvDUc3iYiIziugBFDTWqMG30pP8D7A1a3VCCjBAKyBBmnmNMiSjCnpU9R7AWdJWTDpTBHuARER0cCmEUKISBcRLWpqaiJdwkXFwvIEGvg4DikaROs4DCgB1LbWotxTHnIQ1omWEyEBONWcCpcUvP9v9xLoLEsW4vRxEe4BfVnROhZpcOE4pGgQC+Mw4kupiYiIBhJFKKhtrVX3/3YH4RPeE+hSutR2qQmpkK0yJqROUE+BzpKyEK+Pj2D1RERE9HkMxkRERBegCAUnW0+G3AO4wlOBKm9VSABOiU+By+rCVUOuUu8D7JScDMBEREQxgsGYiIgGPUUoONV2KmQPcKU3+E9noFNt54h3wCW5MDZlLNxWt7oUmgGYiIgotjEYExHRoKEIBXVtdeedAe4IdKjtHPEOyJKMa93XBgPw2YOwEgwJEayeiIiI+guDMRERDThCCNS316vBt/sU6CpvFXwBn9rOFmeDLMmY7Z4NlxQ8BEuWZFiMlghWT0REROHGYExERDFLCIFGX2PPKdCeCvVArHZ/u9ouyZQEl9WFWa5ZwfB7dgZYMkoRrJ6IiIiiBYMxERFFve4A3B166z+px5FTR1DpqUSbv01tl2RKgizJKJKL1Nlfl9UFq9EaweqJiIgo2jEYExFR1BBCoLmjWZ35VZdCeyvR2tWqtrNb7MiIy0CBswCyVVYPwko0JUaweiIiIopVDMZERBR2Qgic7jitBt/u8FvhqUBLV4vazmKwwG11Y0bWjJA9wDlZOWhoaIhgD4iIiGggYTAmIqJ+1exrDjkFutJTiQpvBbydXrWNxWCBy+rCtMxpIUugk03J0Gg0EayeiIiIBgMGYyIiuiTOdJxBpTd4+nN3+K3wVMDT6VHbmA1myJKMqRlT1RlgBmAiIiKKNAZjIiL6Uryd3pD9v93LoE93nFbbxOvj4bK6kJ+er+7/dVldsMfZGYCJiIgo6jAYExHReXk7verS53NvhdTc0ay2idfHQ5ZkTEybCFmSgwdhSW444h0MwERERBQzGIyJiAa51q7W854C3eRrUtvE6eLglJzIS80L3gPY6oJLciElPoUBmIiIiGIegzER0SDR1tWmzgCXe8pR6Q3OBDe095zubNKZ4JScyB2SG3IK9JCEIdBqtBGsnoiIiKj/MBgTEQ0w7f72kMOvug/EOjcAG7QGyJKMMY4xwRngsyE4NSGVAZiIiIgGHQZjIqIY1e5vR5W3Sp397V4OXd9er7YxaA1wSk6MdoxWw69TciLdnM4ATERERHQWgzERUZTz+X2o8lb1nAJ9djn0qbZTahu9Vg+n5MRI+8iQU6AZgImIiIi+GIMxEVGU6Ah0BAPw2eXP3SH4VOspCAgAgE6rQ5YlC8NtwzHLNQuyJMNtdSPdnA6dVhfhHhARERHFJgZjIqIw6wx0qjPA5x6EdbL1ZE8A1uiQacnEsKRhKJKLgjPAkgsZlgzotfyjm4iIiOhS4m9XRET9pDPQiRMtJ4Lh11uu3g+4trUWilAABANwhiUD2YnZKHAWqAdhZVoyGYCJiIiIwoS/dRERfU1dSheqW6rVw6+6l0HXtNaoAVir0SLDnAG31Y3pmdOD9wG2BgOwQWuIcA+IiIiIBjcGYyKiPvIrfjUAn7sHuKalBgERABAMwOnmdMiSjKmZU+GW3JCtMrIsWTDqjBHuARERERGdD4MxEdHn+BU/alpqQu4DXOGpQHVLtRqANdAEA7BVxpSMKXBJLsiSDKfkZAAmIiIiijEMxkQ0aAWUAGpba3vuA3w2CJ9oOYGA0hOAU82pcEku5Kfnq7dBckpOmHSmCPeAiIiIiC4FBmMiGvAUoaC2tVad/e0OwlXeKvgVv9ouNSEVslXGhNQJwT3AUjAAx+njIlg9EREREfU3BmMiGjC6A7C6//dsEK7yVqFL6VLbDUkYApfkwvgh40NmgOP18RGsnoiIiIgihcGYiGKOIhScajulHn5V6alUQ/C5AdgR74Db6sa4lHHqDLBslRmAiYiIiCgEgzERRS1FKKhrqws5AbrCU4EqbxU6Ah1qO0e8A7Ik4zrHdXBb3eq9gBMMCRGsnoiIiIhiBYMxEUWcEAJ17XWo9PTs/+0OwL6AT21nj7NDtsr4lvtbcFmDp0DLkgyL0RLB6omIiIgo1jEYE1HYCCHQ0N6Acm85Kj2V6kFYVd4qtPvb1XbJpmS4rC580/1NdQ+wLMmQjFIEqyciIiKigYrBmIguOSEEGn2NvU6BrvRUos3fprZLMiVBlmQUyUXqHmCX1cUATERERERhFbZgfPDgQWzcuBGKoqCwsBBz584Nef3tt9/G9u3bodPpYLVasWDBAqSkpKC+vh4lJSVQFAWBQACzZ8/GrFmzAABlZWV45pln0NnZidzcXPzgBz+ARqNBS0sLnnzySdTX1yMlJQWLFy+GxcKllkSXmhACTb4mde9v9xLoSm8lWrta1XZWoxUuqwsFzgLIVjm4D1iSkWhKjGD1RERERERBYQnGiqJgw4YNWLFiBex2O5YtW4a8vDxkZWWpbdxuN4qLi2EymbB161Zs3rwZixcvRnJyMh5//HEYDAb4fD4sWbIEeXl5sNlsWL9+Pe6++27k5OTg5z//OQ4ePIjc3Fz84Q9/wOjRozF37lz84Q9/wB/+8AfMnz8/HF0lGpCEEGjuaEaFpwLNdc34Z/U/1QDc0tWitpOMEtxWN76R9Q3IkqwehJVkSopg9UREREREFxeWYFxaWoq0tDSkpqYCAKZMmYL9+/eHBONRo0apP+fk5GDPnj3BAvU9JXZ1dUFRFABAc3Mz2tvbMWzYMADA9OnTsX//fuTm5mL//v1YuXIlAGDGjBlYuXIlgzFRHzX7mkNOge4+EKs7AOv1esRp4uCyujAtc1pwCfTZZdBJpiRoNJoI94CIiIiI6MsJSzBuamqC3W5XH9vtdnz22WcXbL9jxw6MGzdOfdzQ0IDi4mKcPHkS8+fPh81mw7Fjx3pds6mpCQBw5swZJCcnAwCSkpJw5syZS90loph3uuN08P6/3fcBPrsc2tPpUduYDWbIkoyrM69W9//munMhWgUDMBERERENGFF3+Nbu3btRVlamzvgCgMPhQElJCZqamrB69Wrk5+f3+XoajeaCv8Bv27YN27ZtAwAUFxfD4XB8rdr7m16vj/oaKfqc8Z1B2ekyHD99HGWny1DWHPz5tO+02sZsMGNo8lAUXV6Ey5Iuw9Ckobgs+TI44h29/v+j1+vhT/CHuxtEIfjnIUULjkWKBhyHFA1ifRyGJRjbbDY0NjaqjxsbG2Gz2Xq1O3ToEN544w2sXLkSBoPhvNdxOp345JNPMHz48AteMzExEc3NzUhOTkZzczOsVut56yoqKkJRUZH6uKGh4Sv3MRwcDkfU10iR4+309sz+nl0GXeGpwOmOngAcr4+HLMm4yn6Vegskt9UNe5y9918gtQGNbY34PI5DigYchxQtOBYpGnAcUjSIhXGYkZFxwdfCEoyzs7NRW1uLuro62Gw27Nu3D4sWLQppc/z4caxfvx7Lly9HYmLPSbWNjY2QJAlGoxEtLS04evQorr/+eiQnJyM+Ph6ffvopcnJysHv3bsyePRsAkJeXh127dmHu3LnYtWsXJkyYEI5uEoVFS2dLyB7g7kOwmnxNaps4XRyckhMTUidAtsrqHuCU+BQugSYiIiIi+pywBGOdTofbbrsNq1atgqIomDlzJpxOJ7Zs2YLs7Gzk5eVh8+bN8Pl8WLt2LYDg3zgsXboU1dXV2LRpEzQaDYQQmDNnDmRZBgDccccdePbZZ9HZ2Ylx48YhNzcXADB37lw8+eST2LFjh3q7JqJY09bVpt7/tzsEV3mr0NDe8zdxJp0JsiQjd0iuugdYlmQMSRgCrUYbweqJiIiIiGKHRgghIl1EtKipqYl0CRcVC8sT6Mtr97ers77lnnL1IKxzA7BRZ4QsyZClntlfl9UVkQDMcUjRgOOQogXHIkUDjkOKBrEwDiO+lJqIggG4ylsVOgvsqUB9e73axqA1wCk5MdoxOmQGOM2cxhlgIiIiIqJ+wmBMdIl1B+DuWeDuA7FOtZ1S23QH4JH2kXBb3epMcLo5nQGYiIiIiCjMGIyJvqKOQIcagM89CKuurQ4CwR0Keq0eWZYsDLcNxzdd34RsDZ4CnZaQBp1WF+EeEBERERERwGBM9IU6A53BAHx25rd7KfTJ1pNqANZpdcg0Z2J48nBc47pGXQadYc5gACYiIiIiinIMxkRndQY6caLlRDD8eoOHYFV6K1HbWgtFKAAAnUaHDEsGshOzUeAsCN4KSXIh05IJvZb/dyIiIiIiikX8TZ4GnS6lCye8J3pOgT57ENa5AVir0SLDkgG31Y0ZWTN6ZoAtGTBoDRHuARERERERXUoMxjRgdSldqGmpCdn/W+GtQE1LTUgATjenw2V1YVrmNPUgrExLJow6Y4R7QERERERE4cBgTDHPr/iDAfhs+O2eAa5uqUZABAAAGmiQbk6HbJUxNWMqXFLwNkhOyckATEREREQ0yPUpGJeXl8PtdvdzKUQXF1ACqGntmQHuPgirurUaAaUnAKeZ0yBLMvLT89XbIDklJ0w6U4R7QERERERE0ahPwfixxx6DzWbDtGnTMG3aNCQnJ/d3XTSIKUJBbWttcP/vOQdhnWg5Ab/iV9ulJqTCZXVhUtokuKzBPcBZlizE6eMiWD0REREREcWaPgXjF154AR9++CH27NmD1157DcOHD8f06dMxadIkmEychaOvpjsAdy997l4GXeWtQpfSpbYbkjAELsmFvNS8kBngeH18BKsnIiIiIqKBok/BWKfTYcKECZgwYQLa2trw/vvv480338Svf/1rTJw4EUVFRbjiiiv6u1aKUYpQcLL1ZE8APrsMutJbGRKAU+JT4LK6MC5lXHAGWHJBtsoMwERERERE1K++1OFbPp8Pf/vb37Bv3z40NjZiypQpcDgcePrpp5Gbm4s77rijv+qkGKAIBXVtdSGnQFd6gwG4M9CptnPEO+CSXBiTMkY9BVqWZCQYEiJYPRERERERDVZ9CsYffvghdu/ejY8++ghXXHEFCgoKsHTpUhiNwdN8Z8+ejQULFjAYDxJCCNS116mHX3XPBFd5q+AL+NR29jg7ZKuMa93XwmV1qcugzQZzBKsnIiIiIiIK1adg/PLLL2PGjBm45ZZbznvwlsViwa233nqpa6MIE0Kgob1BPfyq0hsMwlXeKrT729V2yaZkuKwufNP9TTX8ypIMyShFsHoiIiIiIqK+6VMwXrNmzRe2KSws/NrFUGQIIdDoawyZ/e1eBn1uAE4yJcFldeEa+ZqQGWAGYCIiIiIiimV9CsYlJSW47rrrMGLECPW5I0eO4E9/+hOWLFnSb8XRpSWEQJOvSd3/e+4e4NauVrVdoikRsiSjSC5Sw6/L6oLVaI1g9URERERERP2jT8H48OHD+OEPfxjy3LBhw7B69ep+KYq+HiEEmjua1eDbHYIrvBUhAVgySnBb3fhG1jcgS3LwICyrjCRTUgSrJyIiIiIiCq8+BWODwQCfz4eEhJ5Tg30+H3Q6Xb8VRl9MCIHTHad7zQBXeCrQ0tWitrMYLHBb3ZiRNaNnBlhyIcmUBI1GE8EeEBERERERRV6fgvHYsWPxwgsv4K677kJCQgLa2tqwYcMGjBs3rr/ro7NOd5xGZW0lDlYeVJc/l3vK4e30qm3MBjNckgvTMqeF7AFONiUzABMREREREV1An4LxzTffjKeffhq33XYbLBYLWlpaMG7cOCxcuLC/6yMAXUoX5v95PjQ6Dfx+PxL0CXBZXZiSPkXd/+uSXLDF2RiAiYiIiIiIvqQ+BWOLxYJly5ahubkZjY2NcDgcSEriPtRwMWgNWJS7CENThyJJJMEeZ2cAJiIiIiIiukT6FIy7JScnIykpCUIIKIoCANBqtf1SGIWa5ZoFh8OBhoaGSJdCREREREQ0oPQpGDc1NWHDhg04cuQIWltbQ17bsmVLvxRGREREREREFA59mu594YUXoNfr8eijjyIuLg5PPPEE8vLycOedd/Z3fURERERERET9qk/B+NNPP8WCBQvgdruh0WjgdruxYMECvP322/1dHxEREREREVG/6lMw1mq16j2LzWYzPB4PTCYTmpqa+rU4IiIiIiIiov7Wpz3Gl19+OT766CNMnDgRY8eOxZNPPgmj0Yjs7Oz+ro+IiIiIiIioX/UpGC9cuBBCCADArbfeirfeegvt7e247rrr+rU4IiIiIiIiov72hcFYURRs3LgRd999NwDAaDTiO9/5Tr8XRkRERERERBQOX7jHWKvV4tChQ9BoNOGoh4iIiIiIiCis+nT41nXXXYdXX30Vfr+/v+shIiIiIiIiCqs+7TH+3//9X5w+fRrvvPMOrFZryGvPPfdcvxRGREREREREFA59Pnzr6zp48CA2btwIRVFQWFiIuXPnhrz+9ttvY/v27dDpdLBarViwYAFSUlJQXl6O9evXo729HVqtFt/+9rcxZcoUAMCjjz6K9vZ2AIDH40F2djYefvhhfPzxx/jFL36BIUOGAAAmTZqEefPmfe0+EBERERER0cDTp2A8cuTIr/UhiqJgw4YNWLFiBex2O5YtW4a8vDxkZWWpbdxuN4qLi2EymbB161Zs3rwZixcvhtFoxP3334/09HQ0NTXhkUcewdixY2E2m/Gzn/1MfX9JSQkmTJigPh4xYgQeeeSRr1U3ERERERERDXx9CsZbtmy54Gs33HDDF76/tLQUaWlpSE1NBQBMmTIF+/fvDwnGo0aNUn/OycnBnj17AAAZGRnq8zabDYmJifB4PDCbzerzbW1t+Pjjj3Hvvff2pTtEREREREREqj4F48bGxpDHp0+fxuHDhzFx4sQ+fUhTUxPsdrv62G6347PPPrtg+x07dmDcuHG9ni8tLYXf71cDdrf9+/dj1KhRSEhIUJ/79NNP8dBDDyE5ORk33XQTnE5nr+tt27YN27ZtAwAUFxfD4XD0qT+Rotfro75GGvg4DikacBxStOBYpGjAcUjRINbHYZ+C8flmYg8ePIj33nvvkhe0e/dulJWVYeXKlSHPNzc34+mnn8Z9990HrTb0MO29e/eioKBAfXzZZZfh2WefRVxcHD788EOsXr0aTz31VK/PKioqQlFRkfq4oaHh0nbmEnM4HFFfIw18HIcUDTgOKVpwLFI04DikaBAL4/Dc1cif16fbNZ3PmDFjsH///j61tdlsIbPOjY2NsNlsvdodOnQIb7zxBh5++GEYDAb1+ba2NhQXF+N73/sehg0bFvIej8eD0tJSXHXVVepzCQkJiIuLAwBcddVVCAQC8Hg8X6p/RERERERENDj0acb41KlTIY87Ojrw3nvv9XmqPDs7G7W1tairq4PNZsO+ffuwaNGikDbHjx/H+vXrsXz5ciQmJqrP+/1+lJSUYPr06cjPz+917Q8++ABXXXUVjEaj+tzp06eRmJgIjUaD0tJSKIoCSZL6VCsRERERERENLn0Kxp8PsUajEZdddhnuu+++Pn2ITqfDbbfdhlWrVkFRFMycORNOpxNbtmxBdnY28vLysHnzZvh8PqxduxZAcCp+6dKl2LdvH44cOQKv14udO3cCAO677z643W4AwL59+3rd+umDDz7A1q1bodPpYDQa8eCDD0Kj0fSpViIiIiIiIhpcNEIIEekiokVNTU2kS7ioWFi3TwMfxyFFA45DihYcixQNOA4pGsTCOPzae4zLy8t7dbKhoQHl5eVfqzAiIiIiIiKiSOtTMH766acRCARCnvP7/fjVr37VL0URERERERERhUufgnFDQ0OvewenpaWhvr6+X4oiIiIiIiIiCpc+BWObzYaysrKQ58rKypCcnNwvRRERERERERGFS59Opb7uuuuwevVq/Mu//AtSU1Nx6tQpvPXWW/j2t7/d3/URERERERER9as+BeOioiKYzWbs2LEDjY2NsNvtuPnmm897X2EiIiIiIiKiWNKnYAwAkydPxuTJk/uzFiIiIiIiIqKw69Me4xdffBFHjx4Nee7o0aP47//+7/6oiYiIiIiIiChs+hSM9+7di+zs7JDnhg4divfee69fiiIiIiIiIiIKlz4FY41GA0VRQp5TFAVCiH4pioiIiIiIiChc+hSMr7jiCvz2t79Vw7GiKHj11VdxxRVX9GtxRERERERERP2tT4dv/eAHP0BxcTHuvvtuOBwONDQ0IDk5GUuXLu3v+oiIiIiIiIj6VZ+Csd1uxxNPPIHS0lI0NjYiMTER+/fvx/Lly/H888/3d41ERERERERE/abPt2tqaWlBaWkpdu7ciYqKCowYMQK33nprP5ZGRERERERE1P8uGoz9fj8OHDiAnTt34h//+AfS0tIwdepUNDQ0YPHixUhMTAxXnURERERERET94qLB+M4774RWq8WMGTPwH//xHxg6dCgAYOvWrWEpjoiILEneZwAAHwlJREFUiIiIiKi/XfRUapfLhdbWVpSWluLYsWNoaWkJV11EREREREREYXHRGeOVK1eivr4eu3btwltvvYWNGzdizJgx6OjoQCAQCFeNRERERERERP3mCw/fSklJwbx58zBv3jx88skn2LVrFzQaDR566CHMnDkT8+fPD0edRET/v737j62qvv84/ro/elsLbeHeC6UNzkptJ5MEhneuAiMQCEtk2UiDbri5Ob8JkV9S/hi/xKz7MrUi46sF3FypjaTOoVtwC3EJgY6U2TFhgETUQC0MCGWsvTXtKKW9Pff7B9y7e29/XVh7f53nI2nsOfe093PqO8Crn8/7cwAAAIAREfWu1JJ0//336/7779ePf/xjffjhh6qvrx+pcQEAAAAAEBO3FYwDHA6HZs2apVmzZg33eAAAAAAAiKlBN98CAAAAACDVEYwBAAAAAKZGMAYAAAAAmBrBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmBrBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmJo9Vm908uRJ1dTUyDAMzZs3T4sWLQp7fd++fTp48KBsNpuys7O1bNkyjRs3TufPn1dVVZWuX78uq9Wq0tJSzZgxQ5K0c+dOffLJJ8rMzJQkrVixQgUFBfL7/aqpqdGJEyeUnp6u5cuXa9KkSbG6VQAAAABAEolJMDYMQ9XV1dq0aZNcLpc2bNggj8ejiRMnBq8pKChQRUWF0tPTtX//ftXW1mrNmjVyOBxauXKl8vLy5PV6tX79ek2dOlWjRo2SJD3xxBMqKSkJe78TJ07oypUrqqys1NmzZ7Vr1y698MILsbhVAAAAAECSiclS6sbGRk2YMEG5ubmy2+2aMWOGjh49GnbNlClTlJ6eLkkqKiqS1+uVJOXn5ysvL0+S5HQ6lZOTo/b29kHf79ixY5o9e7YsFouKi4t17do1tbW1jcCdAQAAAACSXUxmjL1er1wuV/DY5XLp7NmzA15fV1enadOm9Tnf2Ngon8+n3Nzc4Lm3335bv/vd7zRlyhR9//vfV1pamrxer9xud9j7eb1ejR07Nuz7HThwQAcOHJAkVVRUhH1NIrLb7Qk/RqQ+6hCJgDpEoqAWkQioQySCZK/DmPUYR6u+vl5NTU0qLy8PO9/W1qbt27drxYoVslpvTnQ//vjjGjNmjHw+n15//XX94Q9/0OLFi6N+r/nz52v+/PnB45aWlmG5h5HidrsTfoxIfdQhEgF1iERBLSIRUIdIBMlQh/n5+QO+FpOl1E6nU62trcHj1tZWOZ3OPtedOnVKe/fu1dq1a5WWlhY839nZqYqKCi1ZskTFxcXB82PHjpXFYlFaWprmzp2rxsbG4PuF/k8Z6P0AAAAAAIhJMC4sLFRzc7OuXr0qn8+nhoYGeTyesGvOnTunqqoqrV27Vjk5OcHzPp9PW7du1ezZs/tsshXoG/b7/Tp69KjuvvtuSZLH41F9fb38fr/OnDmjzMzMPsuoAQAAAACQYrSU2maz6amnntLzzz8vwzA0d+5c3X333dqzZ48KCwvl8XhUW1urrq4ubdu2TdLNqfh169apoaFBn376qTo6OnTo0CFJ/3ksU2VlZXAjrnvuuUdLly6VJH31q1/V8ePH9cwzz8jhcGj58uWxuE0AAAAAQBKy+P1+f7wHkSguX74c7yEMKhnW7SP1UYdIBNQhEgW1iERAHSIRJEMdxr3HGAAAAACAREUwBgAAAACYGsEYAAAAAGBqBGMAAAAAgKkRjAEAAAAApkYwBgAAAACYGsEYAAAAAGBqBGMAAAAAgKkRjAEAAAAApkYwBgAAAACYGsEYAAAAAGBqBGMAAAAAgKkRjAEAAAAApkYwBgAAAACYGsEYAAAAAGBqBGMAAAAAgKkRjAEAAAAApkYwBgAAAACYGsEYAAAAAGBqBGMAAAAAgKkRjAEAAAAApkYwBgAAAACYGsEYAAAAAGBqBGMAAAAAgKkRjAEAAAAApkYwBgAAAACYGsEYAAAAAGBqBGMAAAAAgKkRjAEAAAAApkYwBgAAAACYGsEYAAAAAGBqBGMAAAAAgKnZY/VGJ0+eVE1NjQzD0Lx587Ro0aKw1/ft26eDBw/KZrMpOztby5Yt07hx43T+/HlVVVXp+vXrslqtKi0t1YwZMyRJlZWV+vzzz2W321VYWKilS5fKbrfr9OnT2rJli8aPHy9J+vrXv67FixfH6lYBAAAAAEkkJsHYMAxVV1dr06ZNcrlc2rBhgzwejyZOnBi8pqCgQBUVFUpPT9f+/ftVW1urNWvWyOFwaOXKlcrLy5PX69X69es1depUjRo1SrNmzdKqVaskSa+++qrq6uq0YMECSdLkyZO1fv36WNweAAAAACCJxWQpdWNjoyZMmKDc3FzZ7XbNmDFDR48eDbtmypQpSk9PlyQVFRXJ6/VKkvLz85WXlydJcjqdysnJUXt7uyRp+vTpslgsslgsuu+++9Ta2hqL2wEAAAAApJCYBGOv1yuXyxU8drlcweDbn7q6Ok2bNq3P+cbGRvl8PuXm5oad9/l8Onz4cNjXnDlzRj/5yU/0wgsv6OLFi8NwFwAAAACAVBSzHuNo1dfXq6mpSeXl5WHn29ratH37dq1YsUJWa3ie37VrlyZPnqzJkydLku6991699tprysjI0PHjx/Xyyy+rsrKyz3sdOHBABw4ckCRVVFTI7XaPzE0NE7vdnvBjROqjDpEIqEMkCmoRiYA6RCJI9jqMSTB2Op1hy5xbW1vldDr7XHfq1Cnt3btX5eXlSktLC57v7OxURUWFlixZouLi4rCveffdd9Xe3q6lS5cGz2VmZgY/nz59uqqrq9Xe3q7s7Oywr50/f77mz58fPG5pabnzm4wBt9ud8GNE6qMOkQioQyQKahGJgDpEIkiGOszPzx/wtZgspS4sLFRzc7OuXr0qn8+nhoYGeTyesGvOnTunqqoqrV27Vjk5OcHzPp9PW7du1ezZs1VSUhL2NQcPHtRHH32ksrKysFnkL774Qn6/X9LN5deGYSgrK2sE7xAAAAAAkKxiMmNss9n01FNP6fnnn5dhGJo7d67uvvtu7dmzR4WFhfJ4PKqtrVVXV5e2bdsm6eZvHNatW6eGhgZ9+umn6ujo0KFDhyRJK1asUEFBgaqqqjRu3Dg9++yzkv7zWKYjR45o//79stlscjgcKisrk8ViicWtAgAAAACSjMUfmFqFLl++HO8hDCoZlicg9VGHSATUIRIFtYhEQB0iESRDHcZ9KTUAAAAAAImKYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEzNHu8BAAAAAADixDBkuXFDunFDlpCPyGNLd7fU1RV2HLyuq0s2q1XW//kfGW53vO/ojhCMAQAAACDR9PbeDJ6BMHoriIadCw2y3d2yRATXQGgNO478up6eOxufwyG/wyF/Rob86elSdvbN75ekCMYAAAAAEA2/X+rpGXwGtb8g218gDXx+63qFXG+5cUPq7b398VksN0Nqerr86elhwdWfmSn/mDE3P09Plz8j42a4DRwHzoUe33pdge8ROHY4JGt4V67b7VZvS8sw/aBjj2AMAAAAILn5/X2D5UBhNCK4Bs8NFWQD1xvG7Y/Par0ZUB2O8OCZkSEjJyc8yIYG1IjgGva1/R2npUkWy/D/fE2AYAwAAABgZAT6V6MJnv31tIYG2SFC6x2x24NBNDATGgiZxtixfcPnrRnYyEAanFUdIMjKTuxKdPwfAgAAAMzG5xt6KXAUYTQ0tIbN2N4Kwv9V/+pAs6PZ2QMvBR4qyEYsDY5cDgzzIhgDAAAAiSC0f/U2dgW22u0a1dra/4zsAMF1WPpXQz9GjZLf6Rx8KXB/M60Ox3/6VwPH/fSvAiMtZsH45MmTqqmpkWEYmjdvnhYtWhT2+r59+3Tw4EHZbDZlZ2dr2bJlGjdunM6fP6+qqipdv35dVqtVpaWlmjFjhiTp6tWreuWVV9TR0aFJkyZp1apVstvt6unp0Y4dO9TU1KSsrCyVlZVp/PjxsbpVAAAApBLDuBkqI3cF7q+ndYhdgQddRtzdfUf9q1aHQxm3lgQrIoiG9a8OMoM61FJg+leR6mISjA3DUHV1tTZt2iSXy6UNGzbI4/Fo4sSJwWsKCgpUUVGh9PR07d+/X7W1tVqzZo0cDodWrlypvLw8eb1erV+/XlOnTtWoUaNUW1urhQsXaubMmfr1r3+turo6LViwQHV1dRo1apS2b9+uDz74QG+99ZbWrFkTi1sFAABArIT2r97pZkoj2b+alha+K3BIcDXGjo1uKXBoaB0gyLonTFBrEu8GDCSCmATjxsZGTZgwQbm5uZKkGTNm6OjRo2HBeMqUKcHPi4qKdPjwYUlSfn5+8LzT6VROTo7a29uVmZmp06dPa/Xq1ZKkOXPm6N1339WCBQt07NgxPfroo5KkkpISvfHGG/L7/bLwGy4AAICR5/NFtxQ4cqY1yl2Bg4+38fnubHzR9q9GsStwfz2t9K8CyScmwdjr9crlcgWPXS6Xzp49O+D1dXV1mjZtWp/zjY2N8vl8ys3NVUdHhzIzM2Wz2STdDM1er7fP+9lsNmVmZqqjo0PZ2dnDeVsAAADJI6J/tb/wOeSuwJFLgUP7V0Nfv5P+Vau171LgkP5Vw+kcfCnwQDsFRx47HCwHBtBHwm2+VV9fr6amJpWXl4edb2tr0/bt27VixQpZh+m3bwcOHNCBAwckSRUVFXK73cPyfUeK3W5P+DEi9VGHSATUIRLFsNTirf5VdXVJt5YFB2ZVQ4+D50KOFTLbGnqsrq6b5wLXB/7r99/++Gw26VboDM6EBo5zcm5+npERDKaB64Lnbs2gBj9ClgGHfl+lpQVX9xFbbw9/JiIRJHsdxiQYO51Otba2Bo9bW1vldDr7XHfq1Cnt3btX5eXlSktLC57v7OxURUWFlixZouLiYklSVlaWOjs71dvbK5vNJq/XG/yegfdzuVzq7e1VZ2ensrKy+rzf/PnzNX/+/OBxS4L3Zrjd7oQfI1IfdYhEQB0iJiL7V/uZQc3JyNC///Wv6JcChx4HHmczQP9qfxE27FxaWv+zow6H/JmZ8jud/e8K3N8y4ohdgUP7V0f8+auD/AwQHf5MRCJIhjoMbdONFJNgXFhYqObmZl29elVOp1MNDQ165plnwq45d+6cqqqqtHHjRuXk5ATP+3w+bd26VbNnz1ZJSUnwvMVi0QMPPKAjR45o5syZOnTokDwejyTpwQcf1KFDh1RcXKwjR47ogQceoL8YAIBUcat/NaqlwJE7BUexK/Dt9K/a7HaNjrwuIoj6HY6B+1dvc1fg0GP6VwFg+Fj8/jtZU3P7jh8/rjfffFOGYWju3LkqLS3Vnj17VFhYKI/Ho82bN+vChQsaM2aMpJu/cVi3bp3q6+v1y1/+MmyjrhUrVqigoED//Oc/9corr+jf//637r33Xq1atUppaWnq7u7Wjh07dO7cOY0ePVplZWXBjb8Gc/ny5RG7/+GQDL+FQeqjDpEIqMME1F//6kCPsxlsV+DAjGx//auB1/+L/tX+Hmcz5K7AkT2tIR9j8/LkvXaN/lXEFX8mIhEkQx0ONmMcs2CcDAjGwNCoQyQC6vA2BJ6/OtCuwJGPtxliBjZyV+DQIHun/av9LgUO9LJGsZlS6C7AfZYCB14foeevUotIBNQhEkEy1GHcl1IDAIAI/fWvDjaDGu1S4IjZVvX03Nn4Av2rIUt7A4HUyMwcfFfgyKXAoaE1NMjGon8VAIAo8LcRAAChQvtXo1kKHDnTGrkUeIAge6fPXw1bChyYVQ0c99e/OsAM6lA9rfSvAgDMhGAMAEh8Ef2r6uyUrbk5bAa1vyDb76xq5FLgiOtkGLc/vtD+1YgZVGP0aCnw/NVoe1ojZ1oDwZb+VQAARgTBGABw5yL7V/sLo5FLgYeafY1YShz4/qH9q3a7XWOjmXG12fpdCuxPT5eRk9NvGO0vuA7V0yq7ncAKAEASIxgDQCrq7e27ydKd7grc31Lg4exfDQTNQGAdPXrIXYGz3G6137jRf/9qyKwt/asAACAa/IsBAGLF77/ZvxoImhGbKQ24FLi/zZkG2BV4WPpXI3YFDn4M1L8a5a7Aw9m/OtrtVneC73wJAACSB8EYAAZ6/mrk42yi3RU4cinwcPevhgRXIyurT09rn+e0DtW/Ggi29K8CAACTIhgDSFyG8Z+geTtLgSNnW6MIsnf8/NV+lgIP2b8a5a7A9K8CAADEBsEYwO0brH81msfbDLUUeLj7V0NmUI3Ro6PbFThyKXDkTKvDIdlsw/tzBQAAQFwQjIFUEehfDQTNaJcC9xduB9gV2HLjhuy9vXJ3dd3ZEAdY0uvPyBi8fzWKXYFDdx3m+asAAAC4HQRjYKQN1b86QJAdclfgkexfDSwHzsqS3O6wnlab06lOn2/opcARM61KS2M5MAAAABISwRjmFehfHWJX4EED6SC7Ag9r/2rEZkrGmDH9LgUOm20dbFfg0Oe63mb/6l1utzrZDRgAAAAphGCMxDNU/2pkkO0vkA6yFHhE+ldvBc9A/2pUuwJHLgWmfxUAAACIC4IxohPav9pfGB1oKfBAOwX3M6saCK3q7b398VksfXYFDn7cdZf8Y8YElwIPuFNwNEGW/lUAAAAg5RCMk53ff3M2NHIp8FBhdLBdgQfoaR3W/tXA81cD/av9BdKhlgLTvwoAAABgGBCMk0TO2rWy9fZqbHt7MLj+V/2rdnvfMBp4nM3Ysf0uBe4z2zrQrsChz3Xl+asAAAAAEhzBOEn4MzKkzEz5bs2wKmIGNaqlwPSvAgAAAEAfBOMk0f6//yuH260OdgMGAAAAgGHFLkIAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFOz+P1+f7wHAQAAAABAvDBjnETWr18f7yEA1CESAnWIREEtIhFQh0gEyV6HBGMAAAAAgKkRjAEAAAAApmYrLy8vj/cgEL1JkybFewgAdYiEQB0iUVCLSATUIRJBMtchm28BAAAAAEyNpdQAAAAAAFOzx3sA6OvkyZOqqamRYRiaN2+eFi1aFPZ6T0+PduzYoaamJmVlZamsrEzjx4+P02iRqoaqw3379ungwYOy2WzKzs7WsmXLNG7cuDiNFqlqqDoMOHLkiLZt26YXX3xRhYWFMR4lUl00ddjQ0KB3331XFotF99xzj1avXh2HkSLVDVWLLS0t2rlzp65duybDMPT4449r+vTpcRotUtVrr72m48ePKycnR7/4xS/6vO73+1VTU6MTJ04oPT1dy5cvT4ol1swYJxjDMFRdXa2NGzfq//7v//TBBx/o0qVLYdfU1dVp1KhR2r59uxYuXKi33norTqNFqoqmDgsKClRRUaGtW7eqpKREtbW1cRotUlU0dShJ169f15/+9CcVFRXFYZRIddHUYXNzs9577z1t3rxZ27Zt05NPPhmfwSKlRVOLv//97/Xwww9ry5YtKisrU3V1dZxGi1Q2Z84cbdy4ccDXT5w4oStXrqiyslJLly7Vrl27Yji6O0cwTjCNjY2aMGGCcnNzZbfbNWPGDB09ejTsmmPHjmnOnDmSpJKSEn388ceiVRzDKZo6nDJlitLT0yVJRUVF8nq98RgqUlg0dShJe/bs0Xe+8x2lpaXFYZRIddHU4cGDB/XNb35To0ePliTl5OTEY6hIcdHUosViUWdnpySps7NTY8eOjcdQkeK+8pWvBP+868+xY8c0e/ZsWSwWFRcX69q1a2pra4vhCO8MwTjBeL1euVyu4LHL5eoTOEKvsdlsyszMVEdHR0zHidQWTR2Gqqur07Rp02IxNJhINHXY1NSklpYWlgpixERTh5cvX1Zzc7Oee+45Pfvsszp58mSshwkTiKYWH330UR0+fFhPP/20XnzxRT311FOxHiYgr9crt9sdPB7q35GJgmAM4L9SX1+vpqYmffvb3473UGAyhmFo9+7d+uEPfxjvocDkDMNQc3OzfvrTn2r16tV6/fXXde3atXgPCyb0wQcfaM6cOfrVr36lDRs2aPv27TIMI97DApICwTjBOJ1Otba2Bo9bW1vldDoHvKa3t1ednZ3KysqK6TiR2qKpQ0k6deqU9u7dq7Vr17KMFcNuqDrs6urSxYsX9bOf/UwrVqzQ2bNntWXLFn3++efxGC5SVLR/L3s8Htntdo0fP155eXlqbm6O9VCR4qKpxbq6Oj388MOSpOLiYvX09LCqEDHndDrV0tISPB7o35GJhmCcYAoLC9Xc3KyrV6/K5/OpoaFBHo8n7JoHH3xQhw4dknRzJ9YHHnhAFoslDqNFqoqmDs+dO6eqqiqtXbuWfjqMiKHqMDMzU9XV1dq5c6d27typoqIirV27ll2pMayi+fPwoYce0unTpyVJ7e3tam5uVm5ubjyGixQWTS263W59/PHHkqRLly6pp6dH2dnZ8RguTMzj8ai+vl5+v19nzpxRZmZmUvS7W/zs2pRwjh8/rjfffFOGYWju3LkqLS3Vnj17VFhYKI/Ho+7ubu3YsUPnzp3T6NGjVVZWxl/AGHZD1eHmzZt14cIFjRkzRtLNv4zXrVsX51Ej1QxVh6HKy8v1xBNPEIwx7IaqQ7/fr927d+vkyZOyWq0qLS3VzJkz4z1spKChavHSpUt6/fXX1dXVJUn6wQ9+oKlTp8Z51Eg1r7zyij755BN1dHQoJydHjz32mHw+nyRpwYIF8vv9qq6u1kcffSSHw6Hly5cnxd/NBGMAAAAAgKmxlBoAAAAAYGoEYwAAAACAqRGMAQAAAACmRjAGAAAAAJgawRgAAAAAYGoEYwAA0MfVq1f12GOPqbe3N95DAQBgxBGMAQAAAACmRjAGAAAAAJiaPd4DAAAA0fF6vXrjjTf06aefKiMjQwsXLtQjjzyid955RxcvXpTVatWJEyeUl5enZcuWqaCgQJJ06dIl7dq1S+fPn5fT6dTjjz8uj8cjSeru7tZvf/tbHTlyRNeuXdOXvvQlPffcc8H3PHz4sPbs2aPu7m4tXLhQpaWlkqTGxkbt2rVLzc3NcjgcmjVrln70ox/F/GcCAMBwIBgDAJAEDMPQSy+9pK997WsqKytTa2urNm/erPz8fEnSsWPHtHr1aq1atUrvv/++Xn75Zb366quSpJdeeklz587Vpk2b9Nlnn2nLli2qqKhQfn6+du/erUuXLunnP/+5xowZo7Nnz8pisQTf97PPPtOrr76qy5cva+PGjXrooYc0ceJE1dTU6JFHHtHs2bPV1dWlCxcuxOXnAgDAcGApNQAASeDzzz9Xe3u7Fi9eLLvdrtzcXM2bN08NDQ2SpEmTJqmkpER2u13f+ta31NPTo7Nnz+rs2bPq6urSokWLZLfbNWXKFE2fPl1/+ctfZBiG/vznP+vJJ5+U0+mU1WrVl7/8ZaWlpQXf99FHH5XD4VBBQYHuuece/eMf/5Ak2e12XblyRe3t7crIyFBxcXFcfi4AAAwHZowBAEgC//rXv9TW1qYnn3wyeM4wDE2ePFlut1sulyt43mq1yuVyqa2tTZLkdrtltf7nd+Hjxo2T1+tVR0eHenp6NGHChAHfd8yYMcHP09PT1dXVJUl6+umntWfPHq1Zs0bjx4/X4sWL9eCDDw7X7QIAEFMEYwAAkoDb7db48eNVWVnZ57V33nlHra2twWPDMNTa2qqxY8dKklpaWmQYRjAct7S0KC8vT1lZWUpLS9OVK1eC/cjRysvLU1lZmQzD0Icffqht27apurpaGRkZd36TAADECUupAQBIAvfdd5/uuusuvffee+ru7pZhGLpw4YIaGxslSU1NTfrb3/6m3t5evf/++0pLS1NRUZGKioqUnp6uP/7xj/L5fDp9+rT+/ve/a+bMmbJarZo7d652794tr9crwzB05swZ9fT0DDme+vp6tbe3y2q1KjMzU5LCZqUBAEgmFr/f74/3IAAAwNC8Xq92796t06dPy+fzKT8/X9/97nf12Wefhe1KPWHCBD399NOaNGmSJOnixYthu1IvWbJEDz30kKSbu1L/5je/0V//+ld1dXWpoKBAzz77rL744gutXLlSb7/9tmw2mySpvLxc3/jGNzRv3jxVVlbq1KlTunHjhsaNG6fvfe97we8JAECyIRgDAJDk3nnnHV25ckXPPPNMvIcCAEBSYs0TAAAAAMDUCMYAAAAAAFNjKTUAAAAAwNSYMQYAAAAAmBrBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmBrBGAAAAABgav8PBtyhECij5cMAAAAASUVORK5CYII=\n","text/plain":["<Figure size 1152x432 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"9915e501"},"source":["## Tuning Summary\n","\n","As we can see below, we managed to improve accuracy and reduce loss by: \n","\n"," 1. Increase embedding_dimension from 32 -> 64\n"," 2. Decreasing learning_rate 0.1 -> 0.01\n","\n","\n","| Tuning | top_1_accuracy |top_5_accuracy | top_10_accuracy | top_50_accuracy | top_100_accuracy|loss|\n","|:---:|:---:|:---:|:---:|:---:|:---:|:---:\n","|**Baseline Model:** embedding_dimension 32, learning_rate 0.1, epochs=5 | 0.0026 | 0.0169 | 0.0317 | 0.1237 | 0.2056 | 891.81 |\n","|**Model_1:** embedding_dimension 32 -> 64, learning_rate 0.1 , epochs=2| 0.0027 | 0.0179 | 0.0341 | 0.1297 | 0.2130 | 903.23 |\n","|**Model_2:** embedding_dimension 64, learning_rate 0.1 -> 0.01, epochs=2| 0.0028 | 0.0189 | 0.0360 | 0.1349 | 0.2205 | 896.75 |"]}]}