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"max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - }, - "model_module_version": "1.2.0" - } - } } }, "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, { "cell_type": "markdown", "metadata": { @@ -3202,99 +150,47 @@ "It handles downloading and preparing the data deterministically and constructing a `tf.data.Dataset` (or `np.array`) in order to enabling easy-to-use and high-performance input pipelines." ] }, + { + "cell_type": "code", + "source": [ + "import os\n", + "os.environ['TF_USE_LEGACY_KERAS'] = '1'" + ], + "metadata": { + "id": "NLu_As-ji2o0" + }, + "execution_count": 1, + "outputs": [] + }, { "cell_type": "code", "metadata": { - "id": "Sciomsbda9NT", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "073a70a6-d0fb-49f7-95ca-d89b89e42994" + "id": "Sciomsbda9NT" }, "source": [ "# Make sure the latest version of TFDS library is installed.\n", "# Older versions are not supported here.\n", "!pip install -q --upgrade tensorflow-datasets\n", - " \n", + "\n", "import tensorflow_datasets as tfds" ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "\u001b[K |████████████████████████████████| 3.7MB 12.1MB/s \n", - "\u001b[?25h" - ], - "name": "stdout" - } - ] + "execution_count": 2, + "outputs": [] }, { "cell_type": "code", "metadata": { "id": "IWY6OFnhLQGx", "colab": { - "base_uri": "https://localhost:8080/", - "height": 581, - "referenced_widgets": [ - "ea5b93232e074516a4080b0ad9a2cdab", - "900f4ad555054a6ca77ed884a45fbf35", - "0963310a6d084fdc8208f1b363a29211", - "0580069a5c57483c9f6846a9aa6db291", - "3b378b731e1f4b8e8fb9816ba81e8b74", - "b236d50d19be4c76bae026d0da730bba", - "9795286e7edf491aafd1e22d521d952f", - "5977c1713b764e66871ced349d9da0b4", - "9326e8a7c4294b179dade4caa38ed166", - "d4555d4b75b843cb92fb2747a2dd42cd", - "1a440a70159647a787d829c78b0a8e70", - "6ae3e6fefc354de1a3a4b37543fa73d0", - "7438901fa0824aa6a646f02e76f5b3cf", - "8ed75df013804400bf92f02d1523346b", - "31eb5e5c9d4145758a411156d54821b8", - "b544800abecc4a27a3ead1140fff34cb", - "acda6ebe40d14e0c900b0dfbe5ce5e4b", - "2666e29dc6d24ad1aa099a75e823c416", - "8ed01c6cb3ab4bdfa00d15cd6f259d37", - "7017189a8391408aa20dd60715dc8706", - "57cab894bd8b439a95cfb4504d5f7a4c", - "1cf5f270aca64dae97420a160561e805", - "3be2b83f46ab48af8130060a9e4c3a44", - "d1650d1d8e394c98aeff889f716c5882", - "c85db6d37ffa4aa999d4cc83c976b6f9", - "19a07375053b41f992cdba81e8bf2a45", - "c11f63b8515b4deeb5aa087137daf740", - "28a2126547e54be387e39aa49fac5f06", - "0286026a05c443659e30ffb06d7d173d", - "94c48efe95eb46569bc2b202c5af4a04", - "8a8275032aa748899183e1bd2462ad9a", - "2f73b9cf76c7461d9501bfbb97b0d8ae", - "58ed1e1811ff4457a32719fdf78acdab", - "961d3dc212884b3ca94e50011ea51f9a", - "5b497e43f2ca4d85968694cdb26b2abe", - 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We load it completely and split\n", " # it manually.\n", @@ -3364,7 +260,7 @@ " reshuffle_each_iteration=False\n", ")\n", "# More info: https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shuffle\n", - " \n", + "\n", "ratings_trainset = ratings_dataset_shuffled.take(trainset_size)\n", "ratings_testset = ratings_dataset_shuffled.skip(trainset_size)\n", "\n", @@ -3375,148 +271,42 @@ " \"ratings_testset size: %d\" % ratings_testset.__len__()\n", ")" ], - "execution_count": null, + "execution_count": 3, "outputs": [ { "output_type": "stream", + "name": "stdout", "text": [ - "\u001b[1mDownloading and preparing dataset 4.70 MiB (download: 4.70 MiB, generated: 32.41 MiB, total: 37.10 MiB) to /root/tensorflow_datasets/movielens/100k-ratings/0.1.0...\u001b[0m\n" - ], - "name": "stdout" - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ea5b93232e074516a4080b0ad9a2cdab", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Completed...', max=1.0, style=Progre…" - ] - }, - "metadata": { - "tags": [] - } - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9326e8a7c4294b179dade4caa38ed166", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Size...', max=1.0, style=ProgressSty…" - ] - }, - "metadata": { - "tags": [] - } - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "acda6ebe40d14e0c900b0dfbe5ce5e4b", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Extraction completed...', max=1.0, styl…" - ] - }, - "metadata": { - "tags": [] - } - }, - { - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "name": "stdout" - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c85db6d37ffa4aa999d4cc83c976b6f9", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Generating splits...', max=1.0, style=ProgressStyle(descr…" - ] - }, - "metadata": { - "tags": [] - } - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "58ed1e1811ff4457a32719fdf78acdab", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Generating train examples...', max=100000.0, style=Progre…" - ] - }, - "metadata": { - "tags": [] - } - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a76bdd5541c94629a93544f9b6d3bae1", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Shuffling movielens-train.tfrecord...', max=100000.0, sty…" - ] - }, - "metadata": { - "tags": [] - } - }, - { - "output_type": "stream", - "text": [ - "\r\u001b[1mDataset movielens downloaded and prepared to /root/tensorflow_datasets/movielens/100k-ratings/0.1.0. Subsequent calls will reuse this data.\u001b[0m\n", - "ratings_dataset size: 100000\n", - " bucketized_user_age movie_genres ... user_rating user_zip_code\n", - "0 45.0 [7] ... 4.0 b'53211'\n", - "1 25.0 [4, 14] ... 2.0 b'80525'\n", - "2 18.0 [4] ... 4.0 b'55439'\n", - "3 50.0 [5, 7] ... 4.0 b'06472'\n", - "4 50.0 [10, 16] ... 3.0 b'75094'\n", + "ratings_dataset size: 100836\n", + " movie_genres movie_id movie_title \\\n", + "0 [7, 8, 13, 15] b'4874' b'K-PAX (2001)' \n", + "1 [7, 18] b'527' b\"Schindler's List (1993)\" \n", + "2 [5, 9] b'7943' b'Killers, The (1946)' \n", + "3 [10, 13, 16] b'1644' b'I Know What You Did Last Summer (1997)' \n", + "4 [1, 2, 3, 4, 12, 14] b'8360' b'Shrek 2 (2004)' \n", "\n", - "[5 rows x 12 columns]\n", - " movie_id movie_title ... user_id user_rating\n", - "0 b'357' b\"One Flew Over the Cuckoo's Nest (1975)\" ... b'138' 4.0\n", - "1 b'709' b'Strictly Ballroom (1992)' ... b'92' 2.0\n", - "2 b'412' b'Very Brady Sequel, A (1996)' ... b'301' 4.0\n", - "3 b'56' b'Pulp Fiction (1994)' ... b'60' 4.0\n", - "4 b'895' b'Scream 2 (1997)' ... b'197' 3.0\n", + " timestamp user_id user_rating \n", + "0 1446749868 b'105' 5.0 \n", + "1 1305696664 b'17' 4.5 \n", + "2 1166068511 b'309' 4.0 \n", + "3 1518640852 b'111' 0.5 \n", + "4 1127221149 b'182' 3.0 \n", + " movie_id movie_title timestamp user_id \\\n", + "0 b'4874' b'K-PAX (2001)' 1446749868 b'105' \n", + "1 b'527' b\"Schindler's List (1993)\" 1305696664 b'17' \n", + "2 b'7943' b'Killers, The (1946)' 1166068511 b'309' \n", + "3 b'1644' b'I Know What You Did Last Summer (1997)' 1518640852 b'111' \n", + "4 b'8360' b'Shrek 2 (2004)' 1127221149 b'182' \n", "\n", - "[5 rows x 5 columns]\n", - "ratings_trainset size: 80000\n", - "ratings_testset size: 20000\n" - ], - "name": "stdout" + " user_rating \n", + "0 5.0 \n", + "1 4.5 \n", + "2 4.0 \n", + "3 0.5 \n", + "4 3.0 \n", + "ratings_trainset size: 80668\n", + "ratings_testset size: 20168\n" + ] } ] }, @@ -3563,7 +353,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "c8286164-3127-41e9-ded5-934b326c65c8" + "outputId": "d5b6473a-b387-4f91-e649-b139f41a62f4" }, "source": [ "from pprint import pprint\n", @@ -3571,18 +361,18 @@ "for rating in ratings_trainset.take(1).as_numpy_iterator():\n", " pprint(rating)" ], - "execution_count": null, + "execution_count": 4, "outputs": [ { "output_type": "stream", + "name": "stdout", "text": [ - "{'movie_id': b'898',\n", - " 'movie_title': b'Postman, The (1997)',\n", - " 'timestamp': 885409515,\n", - " 'user_id': b'681',\n", - " 'user_rating': 4.0}\n" - ], - "name": "stdout" + "{'movie_id': b'3421',\n", + " 'movie_title': b'Animal House (1978)',\n", + " 'timestamp': 975212199,\n", + " 'user_id': b'216',\n", + " 'user_rating': 5.0}\n" + ] } ] }, @@ -3613,12 +403,12 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "13cf6e35-b23c-4ed5-fbde-e2ac000f7bdb" + "outputId": "fdea8df1-9f6a-47c7-a1ae-ed70342b6201" }, "source": [ "# Make a Keras Normalization layer to standardize a numerical feature.\n", "timestamp_normalization_layer = \\\n", - " tf.keras.layers.experimental.preprocessing.Normalization(axis=None)\n", + " tf.keras.layers.Normalization(axis=None)\n", "\n", "# Normalization layer is a non-trainable layer and its state (mean and std of\n", "# feature set) must be set before training in a step called \"adaptation\".\n", @@ -3634,16 +424,16 @@ " f\"Normalized timestamp: {timestamp_normalization_layer(rating['timestamp'])}\"\n", " )" ], - "execution_count": null, + "execution_count": 5, "outputs": [ { "output_type": "stream", + "name": "stdout", "text": [ - "Raw timestamp: 885409515 -> Normalized timestamp: 0.3526017963886261\n", - "Raw timestamp: 883388887 -> Normalized timestamp: -0.026022713631391525\n", - "Raw timestamp: 891249586 -> Normalized timestamp: 1.4468868970870972\n" - ], - "name": "stdout" + "Raw timestamp: 975212199 -> Normalized timestamp: -1.0651628971099854\n", + "Raw timestamp: 1167543227 -> Normalized timestamp: -0.1764659434556961\n", + "Raw timestamp: 958881789 -> Normalized timestamp: -1.1406203508377075\n" + ] } ] }, @@ -3674,12 +464,12 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "d48ddf78-6c1d-4f8f-f1b2-38b9e6962209" + "outputId": "abb36d70-c592-48bf-a40a-0bcef167b7c3" }, "source": [ "# Make a Keras StringLookup layer as the mapping (lookup)\n", "user_id_lookup_layer = \\\n", - " tf.keras.layers.experimental.preprocessing.StringLookup(mask_token=None)\n", + " tf.keras.layers.StringLookup(mask_token=None)\n", "\n", "# StringLookup layer is a non-trainable layer and its state (the vocabulary)\n", "# must be constructed and set before training in a step called \"adaptation\".\n", @@ -3712,11 +502,11 @@ "\n", "user_id_embedding_layer = tf.keras.layers.Embedding(\n", " # Size of the vocabulary\n", - " input_dim=user_id_lookup_layer.vocab_size(),\n", + " input_dim=user_id_lookup_layer.vocabulary_size(),\n", " # Dimension of the dense embedding\n", " output_dim=user_id_embedding_dim\n", ")\n", - " \n", + "\n", "# A model that takes raw string feature values (user_id) in and yields embeddings\n", "user_id_model = tf.keras.Sequential(\n", " [\n", @@ -3724,17 +514,59 @@ " user_id_embedding_layer\n", " ]\n", ")\n", - " \n", + "\n", "print(\n", " \"Embeddings for user ids: ['-2', '13', '655', 'xxx']\\n\",\n", " user_id_model(\n", - " ['-2', '13', '655', 'xxx']\n", + " tf.convert_to_tensor(['-2', '13', '655', 'xxx'])\n", " )\n", - ")\n", - "\n", - "\n", + ")" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Vocabulary[:10] -> ['[UNK]', '414', '599', '474', '448', '274', '610', '68', '380', '606']\n", + "Mapped integer for user ids: ['-2', '13', '655', 'xxx']\n", + " tf.Tensor([ 0 493 0 0], shape=(4,), dtype=int64)\n", + "Embeddings for user ids: ['-2', '13', '655', 'xxx']\n", + " tf.Tensor(\n", + "[[ 0.01478275 -0.03399806 -0.04737892 -0.01455851 0.02260116 0.03772945\n", + " -0.04947149 -0.03576241 0.01529941 -0.01436429 0.03431726 0.00177632\n", + " 0.00778866 0.00945215 -0.02211686 0.04326234 0.00865857 0.03611586\n", + " -0.00819879 -0.01737251 -0.03739591 0.04867731 -0.00277033 0.02783228\n", + " -0.03756921 0.04588716 -0.00360299 -0.00270822 -0.01879736 0.00096357\n", + " 0.04194726 -0.03343551]\n", + " [-0.00042721 0.01578123 0.03790379 0.03472162 -0.04650879 0.02481607\n", + " -0.0494769 0.01245934 0.04805774 0.0247086 0.03865222 0.00027167\n", + " 0.00802004 0.00562286 0.02058078 -0.03171451 0.0408157 0.00758809\n", + " 0.04206653 0.04217685 0.01028284 0.02889507 0.03656546 0.00334147\n", + " -0.00780983 -0.02825767 0.00996434 0.02749718 -0.03737084 -0.0091005\n", + " 0.03157964 0.01717177]\n", + " [ 0.01478275 -0.03399806 -0.04737892 -0.01455851 0.02260116 0.03772945\n", + " -0.04947149 -0.03576241 0.01529941 -0.01436429 0.03431726 0.00177632\n", + " 0.00778866 0.00945215 -0.02211686 0.04326234 0.00865857 0.03611586\n", + " -0.00819879 -0.01737251 -0.03739591 0.04867731 -0.00277033 0.02783228\n", + " -0.03756921 0.04588716 -0.00360299 -0.00270822 -0.01879736 0.00096357\n", + " 0.04194726 -0.03343551]\n", + " [ 0.01478275 -0.03399806 -0.04737892 -0.01455851 0.02260116 0.03772945\n", + " -0.04947149 -0.03576241 0.01529941 -0.01436429 0.03431726 0.00177632\n", + " 0.00778866 0.00945215 -0.02211686 0.04326234 0.00865857 0.03611586\n", + " -0.00819879 -0.01737251 -0.03739591 0.04867731 -0.00277033 0.02783228\n", + " -0.03756921 0.04588716 -0.00360299 -0.00270822 -0.01879736 0.00096357\n", + " 0.04194726 -0.03343551]], shape=(4, 32), dtype=float32)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ "movie_id_lookup_layer = \\\n", - " tf.keras.layers.experimental.preprocessing.StringLookup(mask_token=None)\n", + " tf.keras.layers.StringLookup(mask_token=None)\n", + "\n", "movie_id_lookup_layer.adapt(\n", " ratings_trainset.map(\n", " lambda x: x['movie_id']\n", @@ -3745,10 +577,10 @@ "movie_id_embedding_dim = 32\n", "\n", "movie_id_embedding_layer = tf.keras.layers.Embedding(\n", - " input_dim=movie_id_lookup_layer.vocab_size(),\n", + " input_dim=movie_id_lookup_layer.vocabulary_size(),\n", " output_dim=movie_id_embedding_dim\n", ")\n", - " \n", + "\n", "movie_id_model = tf.keras.Sequential(\n", " [\n", " movie_id_lookup_layer,\n", @@ -3757,84 +589,30 @@ ")\n", "\n", "print(\n", - " f\"Embedding for the movie 898:\\n {movie_id_model('898')}\"\n", + " f\"Embedding for the movie 898:\\n {movie_id_model(tf.convert_to_tensor(['898']))}\"\n", ")" ], - "execution_count": null, + "metadata": { + "id": "O-Fw4HAuO1kh", + "outputId": "be83a6c3-31f7-4cd7-fc1e-83f94074d213", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 7, "outputs": [ { "output_type": "stream", - "text": [ - "Vocabulary[:10] -> ['[UNK]', '405', '655', '13', '450', '276', '303', '416', '537', '234']\n", - "Mapped integer for user ids: ['-2', '13', '655', 'xxx']\n", - " tf.Tensor([0 3 2 0], shape=(4,), dtype=int64)\n", - "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a input: ['-2', '13', '655', 'xxx']\n", - "Consider rewriting this model with the Functional API.\n" - ], - "name": "stdout" - }, - { - "output_type": "stream", - "text": [ - "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a input: ['-2', '13', '655', 'xxx']\n", - "Consider rewriting this model with the Functional API.\n" - ], - "name": "stderr" - }, - { - "output_type": "stream", - "text": [ - "Embeddings for user ids: ['-2', '13', '655', 'xxx']\n", - " tf.Tensor(\n", - "[[ 0.01645621 -0.00589932 -0.01471175 -0.00355174 -0.04663396 0.01846724\n", - " 0.02401174 0.03724445 -0.02736737 -0.02768031 -0.01896119 0.02223358\n", - " -0.03668128 0.00480639 0.00746088 0.03996835 -0.04905364 0.00212307\n", - " 0.01345445 -0.03006717 0.02294225 0.00458346 -0.03924345 0.01767061\n", - " 0.01602763 -0.01630496 0.01014177 -0.02893742 0.03527372 -0.00593783\n", - " 0.04485276 -0.02624741]\n", - " [ 0.04355587 -0.04048269 -0.04138212 0.01247839 -0.01294935 0.00139042\n", - " 0.01233207 0.03024682 -0.03334862 -0.02790955 0.01242272 -0.04128085\n", - " 0.04214266 0.04348017 0.01045523 -0.00205957 -0.03556986 -0.01739997\n", - " 0.04255753 0.02757342 0.0136765 0.01282351 -0.01459817 -0.00855327\n", - " -0.03894869 -0.0358853 -0.03112409 0.01894793 0.02213276 -0.02511839\n", - " 0.00912381 -0.00024097]\n", - " [ 0.00016055 -0.03171784 -0.03682018 0.01463613 0.04559476 0.01670735\n", - " -0.01924447 -0.01310781 -0.0052641 -0.03164054 0.00288255 0.02052755\n", - " 0.0398633 -0.01861371 0.01233826 0.04681553 -0.03879207 -0.02040946\n", - " 0.04356605 -0.03658737 -0.01806207 -0.0237723 -0.04685124 0.04004553\n", - " 0.01409379 0.00821855 -0.02908291 0.02173609 -0.0136477 -0.04532908\n", - " -0.03502221 0.03436175]\n", - " [ 0.01645621 -0.00589932 -0.01471175 -0.00355174 -0.04663396 0.01846724\n", - " 0.02401174 0.03724445 -0.02736737 -0.02768031 -0.01896119 0.02223358\n", - " -0.03668128 0.00480639 0.00746088 0.03996835 -0.04905364 0.00212307\n", - " 0.01345445 -0.03006717 0.02294225 0.00458346 -0.03924345 0.01767061\n", - " 0.01602763 -0.01630496 0.01014177 -0.02893742 0.03527372 -0.00593783\n", - " 0.04485276 -0.02624741]], shape=(4, 32), dtype=float32)\n", - "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a input: 898\n", - "Consider rewriting this model with the Functional API.\n" - ], - "name": "stdout" - }, - { - "output_type": "stream", - "text": [ - "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a input: 898\n", - "Consider rewriting this model with the Functional API.\n" - ], - "name": "stderr" - }, - { - "output_type": "stream", + "name": "stdout", "text": [ "Embedding for the movie 898:\n", - " [-0.00188267 -0.01525087 -0.00824153 -0.04668652 0.005212 -0.02621797\n", - " 0.03498086 -0.03825183 0.02820224 -0.03877431 0.02133146 -0.0108206\n", - " 0.02659546 -0.00487442 -0.02332041 -0.02766312 -0.03729247 -0.02090429\n", - " -0.02203454 0.00766915 0.03768411 -0.00749893 -0.03042971 -0.01427402\n", - " -0.0393108 -0.04658396 -0.00478858 0.01963672 -0.04822909 -0.00989123\n", - " 0.02923337 0.02679086]\n" - ], - "name": "stdout" + " [[ 0.02420554 -0.02993221 -0.03578611 -0.01940017 0.02106949 -0.01936241\n", + " -0.00158745 0.00174867 0.02626561 -0.0172717 0.02571883 -0.03316146\n", + " 0.03160522 0.04094924 -0.01908119 0.01257378 -0.00210525 -0.013066\n", + " -0.03793862 0.01088411 0.02089325 -0.04121248 0.03619212 0.01857083\n", + " 0.02743815 0.03726472 -0.03322572 0.03820366 0.01855929 0.03005471\n", + " -0.01181834 -0.00866457]]\n" + ] } ] }, @@ -3857,13 +635,13 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "3941d2e4-cbc3-442e-d4cb-45210a26ad5f" + "outputId": "a4d65258-0200-4986-8653-fdf07ba98eb7" }, "source": [ "# Keras TextVectorization layer transforms the raw texts into `word pieces` and\n", "# map these pieces into tokens.\n", "movie_title_vectorization_layer = \\\n", - " tf.keras.layers.experimental.preprocessing.TextVectorization()\n", + " tf.keras.layers.TextVectorization()\n", "movie_title_vectorization_layer.adapt(\n", " ratings_trainset.map(\n", " lambda rating: rating['movie_title']\n", @@ -3903,16 +681,16 @@ " ]\n", ")" ], - "execution_count": null, + "execution_count": 8, "outputs": [ { "output_type": "stream", + "name": "stdout", "text": [ - "Vocabulary[40:50] -> ['first', 'contact', '1971', '1977', 'monty', '1983', 'love', 'on', 'last', 'men']\n", + "Vocabulary[40:50] -> ['2014', 'ii', '1985', '2013', 'day', 'wars', '2015', '1982', 'for', 'episode']\n", "Vectorized title for 'Postman, The (1997)'\n", - " tf.Tensor([1120 2 4], shape=(3,), dtype=int64)\n" - ], - "name": "stdout" + " tf.Tensor([1119 2 12], shape=(3,), dtype=int64)\n" + ] } ] }, @@ -3945,11 +723,11 @@ "source": [ "# Query tower\n", "query_model = user_id_model\n", - " \n", + "\n", "# Candidate tower\n", "candidate_model = movie_id_model\n", - " \n", - " \n", + "\n", + "\n", "# Here we only used query and candidate identifiers to buid the towers. This\n", "# corresponds exactly to a classic matrix factorization approach.\n", "# https://ieeexplore.ieee.org/abstract/document/4781121\n", @@ -3963,7 +741,7 @@ "# self.normalized_timestamp(inputs[\"timestamp\"])\n", "# ], axis=1)" ], - "execution_count": null, + "execution_count": 9, "outputs": [] }, { @@ -4000,7 +778,7 @@ " 'movie_id': rating['movie_id'],\n", " }\n", ")\n", - " \n", + "\n", "retrieval_ratings_testset = ratings_testset.map(\n", " lambda rating: {\n", " 'user_id': rating['user_id'],\n", @@ -4008,7 +786,7 @@ " }\n", ")" ], - "execution_count": null, + "execution_count": 10, "outputs": [] }, { @@ -4031,66 +809,15 @@ "metadata": { "id": "DgwkIUn71_0y", "colab": { - "base_uri": "https://localhost:8080/", - "height": 356, - "referenced_widgets": [ - "5836d1a2e06d4417a79a306987ca2a8d", - "4c7f3f0497a442498e29140b13da3718", - "5eac2b4797f342589fdf1a729d69b4c4", - "82bc0ba55b52415ca3c1d092d402f0d8", - "4cead5ce2ec549389930e286008dbd43", - "43a37170841b406ead6ed8a0061290f5", - "f2df5ab944484b9aa5571b1f92fb31ae", - "78a235be12ae486cb85db74c9e0dc696", - "37d9b4c872a042bf83509aabf01e49cf", - "048b5344d1f94581b549a774e27d5b54", - "c8cca219c1194d36bc93f0ce16664154", - "3dd0280309064c14970cd7186b7ba0d6", - "d370bf61eff04e778a1810ed74ce2d45", - "76b9fe27088a42eeb6c861aa61b4218c", - "cb907be1778148688a8b3d344378259e", - "be1e18a683bf474b83414922115b8cb3", - "017c5f33da3a497097df21de70bfd873", - "0bbb513d33884d6ab920498cf07a74a1", - "f5994d027b694fd68d9534f4685b8448", - "e42f9d4198c549da9a0c3abdffc30cd7", - "cae09be9a5f346a2bb7663396474f7ab", - "3ce95d4466bc43019bb09538fe551f2d", - "7122e14af06b4b0e9c9091cf3d325122", - "e1fa286fbc844c6e821b1aa2479a5c61", - "1d1e79c03bb145738ef6fee464a5274e", - "cea1babe04e34a929978ae0ee86d07da", - "e6964e7aaa8f42e293e41ca62f8bb1d3", - "c4a5eb5a8e654be4b269af08a31add41", - "b8425150aca94b619551ee8fe8349738", - "1b30b087eda94047a350ee51248512d5", - "df215de96d744afca92ae23c3ef16263", - "8ab2b2f1658b4a728bbb31b0817a2973", - "380f4d4102b34e60abfed1eb9bdbadd2", - "00f1ec84c2c54d36b5a69eb76f546613", - "bd1f4889292c471992e68a83ab764858", - "d883f0e109ff46f9a449a411a1404025", - "9d5e2fb4988e46fea9cd924e82e13d5d", - "a7318131bcd347c7a3c28fe94bcce41b", - "c0ad928e95664faea5f00efb245e75ec", - "7fafd911808f4381b88aa2956beb3d1c", - "a31a11356ec04a97b6cc7957985fce93", - "8ca8e193f3604436a2b76b062a1b2bfa", - "5641187ab3124a2c97613794ff3a2140", - "87c44927b6fd4fa188a3f2bba1968d26", - "bc2f6fcc89734b43a83c72226f5d6bc0", - "f51b4a5b99e040fd9f9681c5f2451c74", - "dc80ecc2d8974e0bb23120bff7d0e215", - "0ee218a7eed5400cb5b79a338effae5d" - ] + "base_uri": "https://localhost:8080/" }, - "outputId": "5f0d33f7-88fb-4502-8a7d-cdee8a763306" + "outputId": "f868ba6f-c3ae-4536-a977-1557ef45f115" }, "source": [ "# To calculate the factorized top-k categorical accuracy we need the dataset of\n", "# all possible candidates that are used as implicit negatives for evaluation.\n", "movies_dataset, movies_dataset_info = tfds.load(\n", - " name='movielens/100k-movies',\n", + " name='movielens/latest-small-movies',\n", " split='train',\n", " with_info=True\n", ")\n", @@ -4098,141 +825,25 @@ "print(\n", " tfds.as_dataframe(movies_dataset.take(5), movies_dataset_info)\n", ")\n", - " \n", + "\n", "# We are using just `movie_id` feature for making the candidates representation\n", "candidates_corpus_dataset = movies_dataset.map(\n", " lambda movie: movie['movie_id']\n", ")" ], - "execution_count": null, + "execution_count": 11, "outputs": [ { "output_type": "stream", + "name": "stdout", "text": [ - "\u001b[1mDownloading and preparing dataset 4.70 MiB (download: 4.70 MiB, generated: 150.35 KiB, total: 4.84 MiB) to /root/tensorflow_datasets/movielens/100k-movies/0.1.0...\u001b[0m\n" - ], - "name": "stdout" - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5836d1a2e06d4417a79a306987ca2a8d", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Completed...', max=1.0, style=Progre…" - ] - }, - "metadata": { - "tags": [] - } - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "37d9b4c872a042bf83509aabf01e49cf", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Size...', max=1.0, style=ProgressSty…" - ] - }, - "metadata": { - "tags": [] - } - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "017c5f33da3a497097df21de70bfd873", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Extraction completed...', max=1.0, styl…" - ] - }, - "metadata": { - "tags": [] - } - }, - { - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "name": "stdout" - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1d1e79c03bb145738ef6fee464a5274e", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Generating splits...', max=1.0, style=ProgressStyle(descr…" - ] - }, - "metadata": { - "tags": [] - } - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "380f4d4102b34e60abfed1eb9bdbadd2", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Generating train examples...', max=1682.0, style=Progress…" - ] - }, - "metadata": { - "tags": [] - } - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a31a11356ec04a97b6cc7957985fce93", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Shuffling movielens-train.tfrecord...', max=1682.0, style…" - ] - }, - "metadata": { - "tags": [] - } - }, - { - "output_type": "stream", - "text": [ - "\r\u001b[1mDataset movielens downloaded and prepared to /root/tensorflow_datasets/movielens/100k-movies/0.1.0. Subsequent calls will reuse this data.\u001b[0m\n", - " movie_genres movie_id movie_title\n", - "0 [4] b'1681' b'You So Crazy (1994)'\n", - "1 [4, 7] b'1457' b'Love Is All There Is (1996)'\n", - "2 [1, 3] b'500' b'Fly Away Home (1996)'\n", - "3 [0] b'838' b'In the Line of Duty 2 (1987)'\n", - "4 [7] b'1648' b'Niagara, Niagara (1997)'\n" - ], - "name": "stdout" + " movie_genres movie_id movie_title\n", + "0 [4] b'2261' b'One Crazy Summer (1986)'\n", + "1 [10] b'1979' b'Friday the 13th Part VI: Jason Lives (1986)'\n", + "2 [4, 5] b'6143' b'Trail of the Pink Panther (1982)'\n", + "3 [4, 7, 14] b'5856' b'Do You Remember Dolly Bell? (Sjecas li se, D...\n", + "4 [0, 4, 7, 16] b'70728' b'Bronson (2009)'\n" + ] } ] }, @@ -4250,11 +861,7 @@ { "cell_type": "code", "metadata": { - "id": "YPO73Ihx_xQS", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "429718b5-5533-49a2-cc06-ed6379ce37b4" + "id": "YPO73Ihx_xQS" }, "source": [ "!pip install -q scann tensorflow-recommenders\n", @@ -4270,19 +877,8 @@ " )\n", ")" ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "\u001b[K |████████████████████████████████| 61kB 4.1MB/s \n", - "\u001b[K |████████████████████████████████| 394.7MB 43kB/s \n", - "\u001b[K |████████████████████████████████| 11.1MB 11.1MB/s \n", - "\u001b[?25h" - ], - "name": "stdout" - } - ] + "execution_count": 12, + "outputs": [] }, { "cell_type": "markdown", @@ -4310,7 +906,7 @@ "# slower to compute. Consider setting `compute_metrics=False` in Retrieval\n", "# costructor during training to save the time in computing the metrics." ], - "execution_count": null, + "execution_count": 13, "outputs": [] }, { @@ -4333,13 +929,13 @@ "source": [ "class RetrievalModel(tfrs.models.Model):\n", " \"\"\"MovieLens candidate generation model\"\"\"\n", - " \n", + "\n", " def __init__(self, query_model, candidate_model, retrieval_task_layer):\n", " super().__init__()\n", " self.query_model: tf.keras.Model = query_model\n", " self.candidate_model: tf.keras.Model = candidate_model\n", " self.retrieval_task_layer: tf.keras.layers.Layer = retrieval_task_layer\n", - " \n", + "\n", " #def compute_loss(self, features: Dict[Text, tf.Tensor], training=False):\n", " def compute_loss(self, features, training=False) -> tf.Tensor:\n", " query_embeddings = self.query_model(features['user_id'])\n", @@ -4352,7 +948,7 @@ " )\n", " return loss" ], - "execution_count": null, + "execution_count": 14, "outputs": [] }, { @@ -4383,7 +979,7 @@ " )\n", ")" ], - "execution_count": null, + "execution_count": 15, "outputs": [] }, { @@ -4393,7 +989,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "0282f6e9-af6a-49b9-e726-a3cfe593df10" + "outputId": "f17923c4-38d2-4cda-dff0-1242bfff9eae" }, "source": [ "# Shuffle the training data for each epoch.\n", @@ -4407,8 +1003,8 @@ " retrieval_ratings_trainset.shuffle(100_000).batch(8192).cache()\n", "retrieval_cached_ratings_testset = \\\n", " retrieval_ratings_testset.batch(4096).cache()\n", - " \n", - "num_epochs = 5 \n", + "\n", + "num_epochs = 5\n", "history = movielens_retrieval_model.fit(\n", " retrieval_cached_ratings_trainset,\n", " validation_data=retrieval_cached_ratings_testset,\n", @@ -4416,23 +1012,23 @@ " epochs=num_epochs\n", ")" ], - "execution_count": null, + "execution_count": 16, "outputs": [ { "output_type": "stream", + "name": "stdout", "text": [ "Epoch 1/5\n", - "10/10 [==============================] - 33s 3s/step - factorized_top_k/top_1_categorical_accuracy: 1.7500e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0025 - factorized_top_k/top_10_categorical_accuracy: 0.0064 - factorized_top_k/top_50_categorical_accuracy: 0.0655 - factorized_top_k/top_100_categorical_accuracy: 0.1396 - loss: 69844.1065 - regularization_loss: 0.0000e+00 - total_loss: 69844.1065 - val_factorized_top_k/top_1_categorical_accuracy: 0.0024 - val_factorized_top_k/top_5_categorical_accuracy: 0.0148 - val_factorized_top_k/top_10_categorical_accuracy: 0.0314 - val_factorized_top_k/top_50_categorical_accuracy: 0.1550 - val_factorized_top_k/top_100_categorical_accuracy: 0.2723 - val_loss: 28808.9102 - val_regularization_loss: 0.0000e+00 - val_total_loss: 28808.9102\n", + "10/10 [==============================] - 169s 15s/step - factorized_top_k/top_1_categorical_accuracy: 6.1982e-05 - factorized_top_k/top_5_categorical_accuracy: 0.0013 - factorized_top_k/top_10_categorical_accuracy: 0.0031 - factorized_top_k/top_50_categorical_accuracy: 0.0262 - factorized_top_k/top_100_categorical_accuracy: 0.0574 - loss: 71061.1591 - regularization_loss: 0.0000e+00 - total_loss: 71061.1591 - val_factorized_top_k/top_1_categorical_accuracy: 0.0017 - val_factorized_top_k/top_5_categorical_accuracy: 0.0090 - val_factorized_top_k/top_10_categorical_accuracy: 0.0175 - val_factorized_top_k/top_50_categorical_accuracy: 0.0799 - val_factorized_top_k/top_100_categorical_accuracy: 0.1389 - val_loss: 30403.5078 - val_regularization_loss: 0.0000e+00 - val_total_loss: 30403.5078\n", "Epoch 2/5\n", - "10/10 [==============================] - 29s 3s/step - factorized_top_k/top_1_categorical_accuracy: 0.0016 - factorized_top_k/top_5_categorical_accuracy: 0.0143 - factorized_top_k/top_10_categorical_accuracy: 0.0320 - factorized_top_k/top_50_categorical_accuracy: 0.1571 - factorized_top_k/top_100_categorical_accuracy: 0.2805 - loss: 67482.7500 - regularization_loss: 0.0000e+00 - total_loss: 67482.7500 - val_factorized_top_k/top_1_categorical_accuracy: 0.0015 - val_factorized_top_k/top_5_categorical_accuracy: 0.0125 - val_factorized_top_k/top_10_categorical_accuracy: 0.0260 - val_factorized_top_k/top_50_categorical_accuracy: 0.1339 - val_factorized_top_k/top_100_categorical_accuracy: 0.2493 - val_loss: 28339.3379 - val_regularization_loss: 0.0000e+00 - val_total_loss: 28339.3379\n", + "10/10 [==============================] - 128s 13s/step - factorized_top_k/top_1_categorical_accuracy: 8.4296e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0079 - factorized_top_k/top_10_categorical_accuracy: 0.0174 - factorized_top_k/top_50_categorical_accuracy: 0.0816 - factorized_top_k/top_100_categorical_accuracy: 0.1432 - loss: 68374.8374 - regularization_loss: 0.0000e+00 - total_loss: 68374.8374 - val_factorized_top_k/top_1_categorical_accuracy: 7.9334e-04 - val_factorized_top_k/top_5_categorical_accuracy: 0.0065 - val_factorized_top_k/top_10_categorical_accuracy: 0.0140 - val_factorized_top_k/top_50_categorical_accuracy: 0.0683 - val_factorized_top_k/top_100_categorical_accuracy: 0.1245 - val_loss: 29863.0508 - val_regularization_loss: 0.0000e+00 - val_total_loss: 29863.0508\n", "Epoch 3/5\n", - "10/10 [==============================] - 30s 3s/step - factorized_top_k/top_1_categorical_accuracy: 0.0024 - factorized_top_k/top_5_categorical_accuracy: 0.0200 - factorized_top_k/top_10_categorical_accuracy: 0.0427 - factorized_top_k/top_50_categorical_accuracy: 0.1851 - factorized_top_k/top_100_categorical_accuracy: 0.3122 - loss: 66307.1009 - regularization_loss: 0.0000e+00 - total_loss: 66307.1009 - val_factorized_top_k/top_1_categorical_accuracy: 0.0012 - val_factorized_top_k/top_5_categorical_accuracy: 0.0097 - val_factorized_top_k/top_10_categorical_accuracy: 0.0216 - val_factorized_top_k/top_50_categorical_accuracy: 0.1235 - val_factorized_top_k/top_100_categorical_accuracy: 0.2349 - val_loss: 28256.8340 - val_regularization_loss: 0.0000e+00 - val_total_loss: 28256.8340\n", + "10/10 [==============================] - 128s 13s/step - factorized_top_k/top_1_categorical_accuracy: 0.0012 - factorized_top_k/top_5_categorical_accuracy: 0.0118 - factorized_top_k/top_10_categorical_accuracy: 0.0232 - factorized_top_k/top_50_categorical_accuracy: 0.1039 - factorized_top_k/top_100_categorical_accuracy: 0.1754 - loss: 66258.5078 - regularization_loss: 0.0000e+00 - total_loss: 66258.5078 - val_factorized_top_k/top_1_categorical_accuracy: 4.9583e-04 - val_factorized_top_k/top_5_categorical_accuracy: 0.0050 - val_factorized_top_k/top_10_categorical_accuracy: 0.0111 - val_factorized_top_k/top_50_categorical_accuracy: 0.0597 - val_factorized_top_k/top_100_categorical_accuracy: 0.1075 - val_loss: 29832.7773 - val_regularization_loss: 0.0000e+00 - val_total_loss: 29832.7773\n", "Epoch 4/5\n", - "10/10 [==============================] - 30s 3s/step - factorized_top_k/top_1_categorical_accuracy: 0.0023 - factorized_top_k/top_5_categorical_accuracy: 0.0241 - factorized_top_k/top_10_categorical_accuracy: 0.0487 - factorized_top_k/top_50_categorical_accuracy: 0.2023 - factorized_top_k/top_100_categorical_accuracy: 0.3328 - loss: 65604.8991 - regularization_loss: 0.0000e+00 - total_loss: 65604.8991 - val_factorized_top_k/top_1_categorical_accuracy: 0.0010 - val_factorized_top_k/top_5_categorical_accuracy: 0.0071 - val_factorized_top_k/top_10_categorical_accuracy: 0.0179 - val_factorized_top_k/top_50_categorical_accuracy: 0.1152 - val_factorized_top_k/top_100_categorical_accuracy: 0.2237 - val_loss: 28279.2637 - val_regularization_loss: 0.0000e+00 - val_total_loss: 28279.2637\n", + "10/10 [==============================] - 128s 13s/step - factorized_top_k/top_1_categorical_accuracy: 0.0012 - factorized_top_k/top_5_categorical_accuracy: 0.0129 - factorized_top_k/top_10_categorical_accuracy: 0.0270 - factorized_top_k/top_50_categorical_accuracy: 0.1152 - factorized_top_k/top_100_categorical_accuracy: 0.1959 - loss: 64616.1264 - regularization_loss: 0.0000e+00 - total_loss: 64616.1264 - val_factorized_top_k/top_1_categorical_accuracy: 4.4625e-04 - val_factorized_top_k/top_5_categorical_accuracy: 0.0035 - val_factorized_top_k/top_10_categorical_accuracy: 0.0085 - val_factorized_top_k/top_50_categorical_accuracy: 0.0501 - val_factorized_top_k/top_100_categorical_accuracy: 0.0933 - val_loss: 30017.0508 - val_regularization_loss: 0.0000e+00 - val_total_loss: 30017.0508\n", "Epoch 5/5\n", - "10/10 [==============================] - 30s 3s/step - factorized_top_k/top_1_categorical_accuracy: 0.0026 - factorized_top_k/top_5_categorical_accuracy: 0.0261 - factorized_top_k/top_10_categorical_accuracy: 0.0543 - factorized_top_k/top_50_categorical_accuracy: 0.2155 - factorized_top_k/top_100_categorical_accuracy: 0.3483 - loss: 65096.5256 - regularization_loss: 0.0000e+00 - total_loss: 65096.5256 - val_factorized_top_k/top_1_categorical_accuracy: 6.5000e-04 - val_factorized_top_k/top_5_categorical_accuracy: 0.0057 - val_factorized_top_k/top_10_categorical_accuracy: 0.0151 - val_factorized_top_k/top_50_categorical_accuracy: 0.1055 - val_factorized_top_k/top_100_categorical_accuracy: 0.2146 - val_loss: 28346.2773 - val_regularization_loss: 0.0000e+00 - val_total_loss: 28346.2773\n" - ], - "name": "stdout" + "10/10 [==============================] - 128s 13s/step - factorized_top_k/top_1_categorical_accuracy: 0.0010 - factorized_top_k/top_5_categorical_accuracy: 0.0135 - factorized_top_k/top_10_categorical_accuracy: 0.0288 - factorized_top_k/top_50_categorical_accuracy: 0.1269 - factorized_top_k/top_100_categorical_accuracy: 0.2194 - loss: 63291.1729 - regularization_loss: 0.0000e+00 - total_loss: 63291.1729 - val_factorized_top_k/top_1_categorical_accuracy: 1.4875e-04 - val_factorized_top_k/top_5_categorical_accuracy: 0.0030 - val_factorized_top_k/top_10_categorical_accuracy: 0.0073 - val_factorized_top_k/top_50_categorical_accuracy: 0.0445 - val_factorized_top_k/top_100_categorical_accuracy: 0.0849 - val_loss: 30291.8203 - val_regularization_loss: 0.0000e+00 - val_total_loss: 30291.8203\n" + ] } ] }, @@ -4453,9 +1049,9 @@ "id": "3djrQCxhBZCV", "colab": { "base_uri": "https://localhost:8080/", - "height": 295 + "height": 472 }, - "outputId": "52d7a26b-63da-4e7b-9412-3651a3df4215" + "outputId": "fb0af53f-81de-4fc6-fa68-e8a01649b8b9" }, "source": [ "# Plot changes in model loss during training\n", @@ -4469,20 +1065,17 @@ "plt.legend([\"train\", \"test\"], loc=\"upper right\")\n", "plt.show()" ], - "execution_count": null, + "execution_count": 17, "outputs": [ { "output_type": "display_data", "data": { - "image/png": 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\n", 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\n" }, - "metadata": { - "tags": [], - "needs_background": "light" - } + "metadata": {} } ] }, @@ -4491,10 +1084,10 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 295 + "height": 472 }, "id": "Fs0nwFpAL7YI", - "outputId": "8b590906-bc47-4e2a-89c6-e3b2c133332f" + "outputId": "c8b3165d-f6cd-4e87-d19d-e89b30a62445" }, "source": [ "# Plot changes in model accuracy during training\n", @@ -4506,20 +1099,17 @@ "plt.legend([\"train\", \"test\"], loc=\"upper right\")\n", "plt.show()" ], - "execution_count": null, + "execution_count": 18, "outputs": [ { "output_type": "display_data", "data": { - "image/png": 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\n", 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\n" }, - "metadata": { - "tags": [], - "needs_background": "light" - } + "metadata": {} } ] }, @@ -4557,7 +1147,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "e7823633-d280-4413-ac9c-c9ef12bd393c" + "outputId": "38e0ed4a-d4db-4c52-c5f4-4a1f7e6ea979" }, "source": [ "brute_force_layer = tfrs.layers.factorized_top_k.BruteForce(\n", @@ -4575,19 +1165,17 @@ " )\n", ")" ], - "execution_count": null, + "execution_count": 19, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, - "metadata": { - "tags": [] - }, - "execution_count": 17 + "metadata": {}, + "execution_count": 19 } ] }, @@ -4598,7 +1186,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "6f9933ca-9624-4752-833b-343bfb0b3f80" + "outputId": "f6637372-b3c6-41a2-ecf5-c95a08aa6b1a" }, "source": [ "user_id = '42'\n", @@ -4608,14 +1196,14 @@ "\n", "print(f\"Recommendations for user {user_id} using BruteForce: {movie_ids[0, :5]}\")" ], - "execution_count": null, + "execution_count": 20, "outputs": [ { "output_type": "stream", + "name": "stdout", "text": [ - "Recommendations for user 42 using BruteForce: [b'468' b'1043' b'560' b'944' b'140']\n" - ], - "name": "stdout" + "Recommendations for user 42 using BruteForce: [b'2771' b'3142' b'3695' b'3120' b'4207']\n" + ] } ] }, @@ -4639,7 +1227,7 @@ "base_uri": "https://localhost:8080/" }, "id": "2_c77jsdiPp9", - "outputId": "d0a4fd6f-8a27-44dd-84a8-32ebf38ef3ce" + "outputId": "4b775ddb-a1ba-47b0-d396-571a82896834" }, "source": [ "scann_layer = tfrs.layers.factorized_top_k.ScaNN(\n", @@ -4664,14 +1252,14 @@ "\n", "print(f\"Recommendations for user {user_id} using ScaNN: {movie_ids[0, :5]}\")" ], - "execution_count": null, + "execution_count": 21, "outputs": [ { "output_type": "stream", + "name": "stdout", "text": [ - "Recommendations for user 42 using ScaNN: [b'1043' b'944' b'356' b'140' b'560']\n" - ], - "name": "stdout" + "Recommendations for user 42 using ScaNN: [b'3142' b'2574' b'3695' b'4565' b'4442']\n" + ] } ] }, @@ -4692,7 +1280,7 @@ "base_uri": "https://localhost:8080/" }, "id": "Fowdeb85j6OS", - "outputId": "1b301bff-5db0-4460-f4c8-f7e9e443a172" + "outputId": "e9426126-6287-4bba-f8b6-e07ef5d53c16" }, "source": [ "import os\n", @@ -4707,36 +1295,28 @@ " options=tf.saved_model.SaveOptions(namespace_whitelist=[\"Scann\"])\n", ")" ], - "execution_count": null, + "execution_count": 22, "outputs": [ { "output_type": "stream", + "name": "stderr", "text": [ - "WARNING:absl:Found untraced functions such as query_with_exclusions, embedding_layer_call_and_return_conditional_losses, embedding_layer_call_fn, embedding_layer_call_fn, embedding_layer_call_and_return_conditional_losses while saving (showing 5 of 6). These functions will not be directly callable after loading.\n", - "WARNING:absl:Found untraced functions such as query_with_exclusions, embedding_layer_call_and_return_conditional_losses, embedding_layer_call_fn, embedding_layer_call_fn, embedding_layer_call_and_return_conditional_losses while saving (showing 5 of 6). These functions will not be directly callable after loading.\n" - ], - "name": "stderr" - }, - { - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: /tmp/tmplyr89r93/retrieval_model/assets\n" - ], - "name": "stdout" - }, - { - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: /tmp/tmplyr89r93/retrieval_model/assets\n" - ], - "name": "stderr" + "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n", + "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n", + "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n", + "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n" + ] } ] }, { "cell_type": "code", "metadata": { - "id": "UNET0awMnJ-X" + "id": "UNET0awMnJ-X", + "outputId": "ce2eaf05-8e10-44ed-c913-11d1c6e4dc1d", + "colab": { + "base_uri": "https://localhost:8080/" + } }, "source": [ "# Reload the saved model to confirm that it works correctly\n", @@ -4747,8 +1327,23 @@ "\n", "print(f\"Recommendations for user {user_id} using reloaded saved model: {movie_ids[0, :5]}\")" ], - "execution_count": null, - "outputs": [] + "execution_count": 23, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Recommendations for user 42 using reloaded saved model: [b'3142' b'2574' b'3695' b'4565' b'4442']\n" + ] + } + ] }, { "cell_type": "markdown", @@ -4863,7 +1458,7 @@ " )\n", " return loss" ], - "execution_count": null, + "execution_count": 24, "outputs": [] }, { @@ -4890,7 +1485,7 @@ " )\n", ")" ], - "execution_count": null, + "execution_count": 25, "outputs": [] }, { @@ -4900,7 +1495,7 @@ "base_uri": "https://localhost:8080/" }, "id": "JXZykvH6-DvF", - "outputId": "8ed2414d-5cb9-4fbe-b4df-20189d1b909e" + "outputId": "cf398902-4e5e-4038-d2a3-2d5d3b2370f3" }, "source": [ "ranking_ratings_trainset = ratings_trainset.shuffle(100_000).batch(8192).cache()\n", @@ -4913,23 +1508,23 @@ " epochs=5\n", ")" ], - "execution_count": null, + "execution_count": 26, "outputs": [ { "output_type": "stream", + "name": "stdout", "text": [ "Epoch 1/5\n", - "10/10 [==============================] - 2s 144ms/step - root_mean_squared_error: 2.7292 - loss: 6.7427 - regularization_loss: 0.0000e+00 - total_loss: 6.7427 - val_root_mean_squared_error: 1.0694 - val_loss: 1.1080 - val_regularization_loss: 0.0000e+00 - val_total_loss: 1.1080\n", + "10/10 [==============================] - 19s 1s/step - root_mean_squared_error: 2.4954 - loss: 5.6800 - regularization_loss: 0.0000e+00 - total_loss: 5.6800 - val_root_mean_squared_error: 0.9894 - val_loss: 0.9845 - val_regularization_loss: 0.0000e+00 - val_total_loss: 0.9845\n", "Epoch 2/5\n", - "10/10 [==============================] - 1s 77ms/step - root_mean_squared_error: 1.0666 - loss: 1.1324 - regularization_loss: 0.0000e+00 - total_loss: 1.1324 - val_root_mean_squared_error: 1.0556 - val_loss: 1.0822 - val_regularization_loss: 0.0000e+00 - val_total_loss: 1.0822\n", + "10/10 [==============================] - 1s 91ms/step - root_mean_squared_error: 0.9699 - loss: 0.9353 - regularization_loss: 0.0000e+00 - total_loss: 0.9353 - val_root_mean_squared_error: 0.9536 - val_loss: 0.9175 - val_regularization_loss: 0.0000e+00 - val_total_loss: 0.9175\n", "Epoch 3/5\n", - "10/10 [==============================] - 1s 61ms/step - root_mean_squared_error: 1.0394 - loss: 1.0736 - regularization_loss: 0.0000e+00 - total_loss: 1.0736 - val_root_mean_squared_error: 1.0142 - val_loss: 0.9957 - val_regularization_loss: 0.0000e+00 - val_total_loss: 0.9957\n", + "10/10 [==============================] - 1s 88ms/step - root_mean_squared_error: 0.9457 - loss: 0.8907 - regularization_loss: 0.0000e+00 - total_loss: 0.8907 - val_root_mean_squared_error: 0.9412 - val_loss: 0.8968 - val_regularization_loss: 0.0000e+00 - val_total_loss: 0.8968\n", "Epoch 4/5\n", - "10/10 [==============================] - 1s 62ms/step - root_mean_squared_error: 1.0131 - loss: 1.0225 - regularization_loss: 0.0000e+00 - total_loss: 1.0225 - val_root_mean_squared_error: 0.9991 - val_loss: 0.9650 - val_regularization_loss: 0.0000e+00 - val_total_loss: 0.9650\n", + "10/10 [==============================] - 1s 81ms/step - root_mean_squared_error: 0.9343 - loss: 0.8695 - regularization_loss: 0.0000e+00 - total_loss: 0.8695 - val_root_mean_squared_error: 0.9334 - val_loss: 0.8839 - val_regularization_loss: 0.0000e+00 - val_total_loss: 0.8839\n", "Epoch 5/5\n", - "10/10 [==============================] - 1s 61ms/step - root_mean_squared_error: 1.0068 - loss: 1.0112 - regularization_loss: 0.0000e+00 - total_loss: 1.0112 - val_root_mean_squared_error: 0.9986 - val_loss: 0.9639 - val_regularization_loss: 0.0000e+00 - val_total_loss: 0.9639\n" - ], - "name": "stdout" + "10/10 [==============================] - 1s 76ms/step - root_mean_squared_error: 0.9266 - loss: 0.8554 - regularization_loss: 0.0000e+00 - total_loss: 0.8554 - val_root_mean_squared_error: 0.9277 - val_loss: 0.8744 - val_regularization_loss: 0.0000e+00 - val_total_loss: 0.8744\n" + ] } ] }, @@ -4938,10 +1533,10 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 295 + "height": 472 }, "id": "1-uNjmoY-gsA", - "outputId": "611bee0c-cce8-4a2a-8f6e-77d828180abe" + "outputId": "8fbd9476-ab44-4a1d-c1ac-b4ed06252b08" }, "source": [ "# Plot changes in model loss during training\n", @@ -4953,20 +1548,17 @@ "plt.legend([\"train\", \"test\"], loc=\"upper right\")\n", "plt.show()" ], - "execution_count": null, + "execution_count": 27, "outputs": [ { "output_type": "display_data", "data": { - "image/png": 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UAwcO5KGHHmLZsmXnbOtrljiCJLlRHO/d04WIsBD6jlnA/I2+Lf1ujCn7PMuqf/755wwYMIAuXbrQunVrevfuzbFjx1i1ahUpKSkkJSXx5JNP8thjjwEwfPhwunfvTrdu3Xwel1/LqpcW/iqr7gv7jmYweNxiNu8/znO923BL+/yqsBhjAs3KqpexsuoVSa2YSFJHdqFjozgeSF3BqHlb7FoPY0ypZomjFIiJDGf80I70aFuXZ2ev5+8z15CdY8nDGFM62ayqUiIiLJSX+iZRKyaCsd9sY/+x07zYN4nI8NBgh2ZMhaWquLd4KNeKOsphPY5SJCRE+OsNLXnshhZ8unovg95czJGTdlMoY4IhMjKSgwcPlvuhY1Xl4MGDREZ6f/8g63GUQsMuv4CaMZE8mLqC3qOd+3rUrVY52GEZU6EkJiaSnp7OgQPlf8ZjZGQkiYneT8zx5x0AxwE3AvtVtVU+65sDbwHtgb+q6n/c5fWBiUAtQIHXVfVld93fgbuB3H/Jv6jqLH99hmDq0bYu8VUrMWLiUm55zUkezWpHBzssYyqM8PBwGjduHOwwSiV/DlWNB7oXsv4Q8EfgP3mWZwF/UtWWQGfgdyLS0mP9i6qa5D7KZdLIdcmF8aSO7EKOKr1Hf8/CrQeDHZIxxvgvcajqfJzkUND6/aq6BMjMs3yPqi5znx8D1gH1/BVnadeiTgwz7r2EWjGRDHpzMZ+s3BPskIwxFVypPjkuIo2AdsAij8W/F5GVIjJORKoXsu1wEUkTkbSyPkaZWD2K6SO70CYxlt9PXsZb320LdkjGmAqs1CYOEakKvAfcr6pH3cWjgAuBJGAP8HxB26vq66qarKrJCQkJfo/X36pFVeKdYZ24ukUtnvxoLf/6dB05dq2HMSYISmXiEJFwnKQxSVVn5C5X1X2qmq2qOcBYICVYMQZDZHgoowZ2YGDnBoz5eisPpC7nTFZOsMMyxlQwpW46rjhX27wJrFPVF/Ksq6OquYP8NwOrAx1fsIWGCP/s2Yo6sZX592cb+On4GUYNbE90ZHiwQzPGVBB+K3IoIpOBrkA8sA94AggHUNXRIlIbSANigBzgONASaAN8A6xyl4M77VZE3sYZplLgR2CERyIpUGkuclgS09J28siMVTSrFc34oR2pGe39BTzGGHM+BRU5tOq4Zdy8Dfu5d9Iy4qpUYsLQFC5MqBrskIwx5YRVxy2nujaryeS7O3PqTDa9R33Psh0/BzskY0w5Z4mjHGhbvxoz7r2EmMrhDBi7kC/W7gt2SMaYcswSRznRsEYV3rvnEi6qFc3wt9OYvHhHsEMyxpRTljjKkfiqEUy+uzO/uSiBR2es4sXPN5b7yp7GmMCzxFHOVIkIY+ygZG7rkMjLX27i0RmryMq2az2MMb5T6q7jMCUXHhrCc73bUDs2kv9+tZkDx07z3wHtiKpk/9zGmJKzHkc5JSL86ZpmPNWrFXM37GfA2EUcPH462GEZY8oBSxzl3MDODRk1sAPr9hyl9+gF7Dx0MtghGWPKOEscFcC1F9dm0rBOHDpxhptf+57Vu44EOyRjTBlmiaOCSG4Ux3v3dCEiLIS+YxYwf2PZLjVvjAkeSxyFyc6EcjSdtUnNaGbcewn146IYOn4JM5alBzskY0wZZImjMF8/B691hq//DYe2Bjsan6gVE0nqyC50bBTHA6krGDVvi13rYYwpEkschanZHCpXh7lPwSvtYOyVsOA1OLY32JGVSExkOOOHdqRH27o8O3s9T360lmy7KZQxxktWHdcbh3fCmhmwajrsXQkINLoMWveGFj0gKs5nsQZSTo7yr0/XMfabbVzfujYv9EkiMjw02GEZY0oJK6vuq7LqBzbC6ulOEjm0BULCoclvnSTS7DqoVMU37xNAb3yzlac+WUdK4zjG3pFMbJTdFMoYY4nD9/fjUIU9y50EsnoGHNsN4VFO8mjV20kmYZV8+55+NHPFbh5MXUGj+CjGD0mhbrXKwQ7JGBNkljj8eSOnnBzY8b2TRNZ+CKcOQWQ1aNnDSSKNLoOQ0j8E9P2WnxgxcSlVIsKYMDSFZrWjgx2SMSaIgnIjJxEZJyL7RSTfe4OLSHMRWSAip0XkwTzruovIBhHZLCKPeCxvLCKL3OVTRST4P+tDQpzkcNNL8OBGGDANLrrW6YlM7AEvtITZj0L60lI9vfeSC+NJHdmFHFV6j/6ehVsPBjskY0wp5Nceh4j8Bude4hNVtVU+62sCDYFewM+q+h93eSiwEbgaSAeWAP1Vda2IpAIzVHWKiIwGVqjqqMLiCNqtY8+chI2zYfV7sGkOZJ+B6o2h1a3OOZGaLQIfkxfSfz7JnW8tYcfBk7zUL4nrW9cJdkjGmCAISo9DVecDhwpZv19VlwCZeValAJtVdauqngGmAD1FRIArgeluuwk4Sad0qhQFrW6BfpPgwU3Q839QvRF8+4Jzfchrl8A3L8DP24Md6VkSq0cxfWQX2iTG8rt3lzH+u23BDskYU4qU1us46gE7PV6nu8tqAIdVNSvP8nOIyHARSRORtAMHSkF5jcrVoN1AGPQB/GkDXPecMwPryyfh5TbwxtWwaAwc3x/sSAGoFlWJd4Z14uoWtfj7R2v516fryLFrPYwxlN7EUWKq+rqqJqtqckJCQrDDOVvVmtBpBAz7HO5bCVc9AWdOwKd/huebwcRe8MM7cOpwUMOMDA9l1MAODOzcgDFfb+WB1OWcybKbQhlT0ZXWO/vsAup7vE50lx0EqolImNvryF1edlVvCJc/4Dz2r3On906HD38HH/8fNL3GOSdyUXdn6CvAQkOEf/ZsRZ3Yyvz7sw0cPHGGUQM7UDWitP6vY4zxt9La41gCNHVnUFUC+gEz1TmTPxfo7bYbDHwYpBh9r2YLuOpx+ONyGPYVdBwG6Utg+hD4T1OYMRw2znGKLwaQiPC7bk34d+82fL/lIH3HLGD/sYyAxmCMKT38PatqMtAViAf2AU8A4QCqOlpEagNpQAyQgzMDq6WqHhWR64GXgFBgnKo+7e7zApyT5XHAD8BAVS301nZBm1XlCznZ8OO3Ti9k7UzIOAyV46BlT2h9GzTo4kwHDpB5G/Zz76RlxFWpxMShKVyQUDVg722MCSy7ALCsJg5PWWdgy5fOcNaGWZB5EmLqwcU3O9N76ySBiN/DWLHzMEPHLyFHlTfv7Ej7BtX9/p7GmMCzxFEeEoenMydgw6dOEtn8BeRkQo0mzpXqrXtDfFO/vv32gycYNG4x+45m8Gr/9vy2ZS2/vp8xJvAscZS3xOHp5CFYN9NJIj9+CyjUbuMkkFa3QmyiX972p+OnGTp+Cat3HeH/3dyafikN/PI+xpjgsMRRnhOHp6N7YM37zjmRXUudZQ0ugda3QsteUCXep2934nQWv3t3GfM2HOD+3zblvquaIgEYLjPG+J8ljoqSODwd3OLUy1o9HQ6sBwmFC7s5w1nNb4DIGJ+8TWZ2Dn+ZsYppS9Ppn1Kfp3q1JjTEkocxZZ0ljoqYOHKpwr417n1E3oMjOyAs0inE2Kq3c61IeGQJ30J5fs5GXp27meta1ealfklEhJX+isDGmIJZ4qjIicOTqnNtyKppzpDWiQMQEQPNb3SGsxp3hdDiX9z35rfb+OfHa7m8aTxj7uhAVCW7UNCYssoShyWOc2VnwY/znV7Iuo/g9BGokuCcC2ndGxJTinWNSGraTh55byXtGlRn3J0dia1sdxQ0piyyxGGJo3CZGbD5c2dm1sbZkJUBsfV/LQFfq1WRrhGZvXoPf5j8A01qRjNxaAoJ0RF+DN4Y4w+WOCxxeO/0MVg/yzknsuUryMmC+Ga/Tu+tcaFXu5m/8QAj3l5K7dhI3r4rhcTqga+1ZYwpPkscljiK58RBWPuBczOq7d85y+q2d5LIxbdATOE3eVq6/RBD3lpClYgw3r6rE01qWokSY8oKSxyWOEruyC5YM8MZztqzHBDnlrmtbnVqZ0XF5bvZ2t1HGTRuETkKE4em0KpebGDjNsYUiyUOSxy+9dNmd3rvdDi4CULCoclV0H4wNL/+nObbfjrBwDcWcfRUJm/e2ZGUxvknGWNM6RGUW8eaciy+CXR9BH6/BEbMh873wN7VMKU/LH/3nOaN46sw/Z4u1IyJ4I43FzF3Q+m406ExpugscZiSEYE6beGaf8J9y6HxFTDzj7Dtm3Oa1omtTOqILjStVZW7J6Tx0YrdQQjYGFNSljiM74SGQ5+JEHcBTB0IP206p0mNqhG8e3dn2jeozh+n/MDkxTuCEKgxpiQscRjfqlwNbk+FkDCYdJszKyuPmMhwJgxNoetFCTw6YxWjv94ShECNMcVlicP4XvVG0H8yHN0NU2+HrHNv0Fi5Uihj7kjmprZ1eebT9Tw3ez0VYaKGMeWB3xKHiIwTkf0isrqA9SIir4jIZhFZKSLt3eXdRGS5xyNDRHq568aLyDaPdUn+it+UUP0UuHk07FgAH/7OqZGVR6WwEF7qm8SATg14bd4WHv9wNTk5ljyMKe38WYFuPPAqMLGA9dcBTd1HJ2AU0ElV5wJJACISB2wG5nhs95CqTvdTzMaXWt0Ch7bCV/90znt0+8s5TUJDhKd7tSImMpzRX2/hWEYW/7mtLeGh1hk2prTyW+JQ1fki0qiQJj2BieqMTywUkWoiUkdV93i06Q18qqon/RWn8bPL/+Qkj6+fdZJH237nNBERHrmuOTGVw3hu9gaOZ2Txv9vbExluZdmNKY2C+bOuHrDT43W6u8xTP2BynmVPu0NbL4pIgZXzRGS4iKSJSNqBAwd8E7EpOhG48SVodDl8+Hv48bsCm97btQlP9WrFVxv2M3jcYo5lZAYwUGOMt0rteICI1AFaA595LH4UaA50BOKAhwvaXlVfV9VkVU1OSEjwa6zmPMIqQd+3Ia6xc7L8YMGzqAZ2bshLfZNYuv1nbn9jEYdOnAlgoMYYbwQzcewC6nu8TnSX5eoDvK+qv/zsVNU96jgNvAWkBCRSU3KVq8OAVJAQmNQbTh4qsGnPpHq8PqgDG/Yeo8+YBew9khHAQI0x5xPMxDETGOTOruoMHMlzfqM/eYap3F4IIiJALyDfGVumlIprDP0mO8USp+Q/TTfXlc1rMWFoCnuPZNB79Pf8+NOJAAZqjCmMP6fjTgYWAM1EJF1E7hKRkSIy0m0yC9iKM2tqLHCvx7aNcHojX+fZ7SQRWQWsAuKBp/wVv/GTBp2g12uw43uY+Yd8p+nm6nxBDSbf3ZkTp7O4bcwC1u89GsBAjTEFseq4Jji+/jfMfQq6PuoUSyzEpn3HuOPNxZzKzOatIR1p36B6gII0pmKz6rimdPnNg9B2AMz7F6xMLbRp01rRTBvZhWpR4Qx8YxHfbvopQEEaY/JjicMEhwjc9DI0vMy5snz794U2rx8XxbSRXWgQF8XQ8UuYvXpvgAI1xuRlicMET+403WoNnJPlhUzTBagZHcmU4Z25uF4M905ayvSl6QEK1BjjyavEISL3iUiMOwPqTRFZJiLX+Ds4UwFExcHt05zn7/YpdJouQLWoSrxzVycuuTCeB6et4K3vtgUgSGOMJ297HENV9ShwDVAduAN4xm9RmYol7gLo9y4c3gFT74Cswi/6qxIRxpt3JtP94to8+dFaXv5ik1XWNSaAvE0c4v73euBtVV3jscyYkmvYBXq+Btu/hY/+WOg0XYCIsFBeHdCO3h0SefGLjfzz43VWWdeYAPG2yOFSEZkDNAYeFZFoIMd/YZkKqc1tTkHEef8P4i6EKx4qtHlYaAjP3dqG6Mgwxn23jaMZmTxzS2vCrLKuMX7lbeK4C6fU+VZVPemWOx/iv7BMhXXFn53kMfcp50rz1r0LbR4SIvztxpbEVg7npS82cTwji5f7JxERZpV1jfEXb3+adQE2qOphERkIPAYc8V9YpsISgR6vQMNL4YN7YMdCLzYR7v/tRfztxpbMXrOXYRPSOHkmKwDBGlMxeZs4RgEnRaQt8CdgCwXfoMmYkgmLgL7vQGx9mDLA6YF4Yehljfl37zZ8t/knBr6xiCMnrSy7Mf7gbeLIcm+41BN4VVX/B0T7LyxT4eVO09UcmNQHTv3s1Wa3Jdfntdvbs3rXUfq+voD9x6yyrjG+5qLtu4EAAB1cSURBVG3iOCYij+JMw/1EREKAcP+FZQxQ40J3mu52r6bp5ureqg7j7uzI9oMn6TN6Aek/2w0kjfElbxNHX+A0zvUce3HunfFvv0VlTK6Gl0CPV+HHb+Dj+887TTfXZU3jeWdYJw6dOEPvUQvYvP+YnwM1puLwKnG4yWISECsiNwIZqmrnOExgtO0LVzwCyyfBN897vVmHhtWZOqILWTlKnzELWZVu8zmM8QVvS470ARYDt+HcmW+RiBQ+T9IYX+r6CLTuA1/9E1a/5/VmLerEMH1kFyqHh9J/7EIWbT3oxyCNqRi8Har6K9BRVQer6iCcW7Y+7r+wjMlDBHq+Cg26wPv3wI5FXm/aKL4K0+/pQq2YCAaNW8zc9fv9GKgx5Z+3iSNEVT2/bQe92VZExonIfhHJ9xavbtHEV0Rks4isFJH2HuuyRWS5+5jpsbyxiCxyt5kqIpW8/AymrAuLgL6TIKYuTOkPh7wvcFgntjKpI7pwUa1o7p6YxswVu/0YqDHlm7eJY7aIfCYid4rIncAnOLd+PZ/xQPdC1l8HNHUfw3GuF8l1SlWT3EcPj+XPAi+qahPgZ5yr2k1FUaUG3D4dcrKdarpeTtMFqFE1gnfv7kT7htW5b8oPTFq03Y+BGlN+eXty/CHgdaCN+3hdVR/2Yrv5QGF1snsCE9WxEKgmInUKaiwiAlwJTHcXTQB6efMZTDkS38SZpntoG6QO8nqaLkB0ZDgTh6bQrVlN/vr+akbNK/weIMaYc3ldDU5V31PVB9zH+z56/3rATo/X6e4ygEgRSRORhSKSmxxqAIdVNSuf9mcRkeHu9mkHDhzwUbim1Gh0KfT4L2ybD5/8n9fTdAEiw0MZc0cHerSty7Oz1/Ps7PVWlt2YIii0yKGIHAPy+0YJoKoa45eoHA1VdZeIXAB8JSKrKEJ9LFV9HaeXRHJysv1VKI+S+jvlSOY/51TTvfwBrzcNDw3hxb5JREeGMWreFo6eyuQfPVsRGmJ3CzDmfApNHKrq77Iiu4D6Hq8T3WWoau5/t4rIPKAd8B7OcFaY2+v4pb2poLr9xUkeXz7pVNO9+GavNw0NEZ7q1YrYyuG8Nm8LxzKyeL5PW8KtLLsxhQr2N2QmMMidXdUZOKKqe0SkuohEAIhIPHApsNatlzUXyL2GZDDwYTACN6WECPT8H9TvDDNGwM4lRdxc+HP35jzcvTkzV+xmxNtLycjM9lOwxpQPfk0cIjIZWAA0E5F0EblLREaKyEi3ySxgK7AZGAvc6y5vAaSJyAqcRPGMqq511z0MPCAim3HOebzpz89gyoDwSOg3CWLqwOR+8POPRd7FPV0v5OmbWzF3w34GjVvMsQyrrGtMQaQinBRMTk7WtLS0YIdh/O3ARnjzt1C1Ntw1BypXK/IuZq7YzQNTl9O8TjQThqRQo2qEHwI1pmwQkaWqmpx3ebCHqozxnYSLnAsED211pulmF73X0KNtXcYOSmbTvuP0GbOAPUdO+SFQY8o2SxymfGl8uXMHwW1fwycPFGmabq5uzWsycWgK+46epveoBfz40wk/BGpM2WWJw5Q/SQPg8gdh2UT47uVi7aLTBTWYfHdnTmVm03v0AtbtOerjII0puyxxmPKp21/h4lvgiydgzQfF2kXrxFhSR3QhLEToO2YBS7d7X97EmPLMEocpn0JCoNcoSEyB90dAevEmRzSpWZVpI7sQV6USA99YxDebrAqBMZY4TPkVHgn9J0PVWu403eIVNawfF0XqyC40rBHFXePTmL16j48DNaZsscRhyrcq8XD7NKcQ4rt9IKN4dwGsGR3J1OFdaFUvhnsnLWNa2s7zb2RMOWWJw5R/Cc2g79twcDOkDi7WNF2A2Khw3hnWiUubxPPQ9JWM+9b7+4EYU55Y4jAVwwVXwI0vwda5MOvBYk3TBYiqFMYbg5PpfnFt/vHxWl78fKNV1jUVjiUOU3G0vwMuewCWjofv/1vs3USEhfLqgHbc1iGRl7/cxJMfrSUnx5KHqTgKrY5rTLlz5ePOleWf/w2qN4KWPc67SX7CQkN49tY2REeGM+67bRzLyOLZW1sTZpV1TQVgicNULCEhcPNoOLoLZgyH2HpQr0MxdyU8fmMLYiuH8+IXGzl+OpNX+rcjIizUx0EbU7rYzyNT8YRXhn6ToWoCvNsPDu8o9q5EhPt+25QnbmrJZ2v2cdf4NE6czjr/hsaUYZY4TMVUNQEGTIOs0/Bu32JP08015NLGPH9bWxZsPcjANxdx+KT390E3pqyxxGEqrprNoc8E+GkjTBsC2SXrKdzaIZHXbm/Pml1H6ff6QvYfy/BRoMaULpY4TMV2YTe44QXY8iV8+lCxp+nmuvbi2oy7syM7Dp3kttEL2HnopI8CNab0sMRhTIfBcOn9kDYOFvyvxLu7rGk87wzrxOGTmfQe/T2b9h3zQZDGlB5+SxwiMk5E9ovI6gLWi4i8IiKbRWSliLR3lyeJyAIRWeMu7+uxzXgR2SYiy91Hkr/iNxXMVU9Ay54w5zFY93GJd9e+QXWmjuhMjkKfMQtYmX7YB0EaUzr4s8cxHuheyPrrgKbuYzgwyl1+Ehikqhe7278kIp73AH1IVZPcx3Lfh20qpJAQuHkM1GsPM+6GXctKvMvmtWOYNqILVSLCGDB2EQu3HvRBoMYEn98Sh6rOBw4V0qQnMFEdC4FqIlJHVTeq6iZ3H7uB/UCCv+I05hfhlaH/FIiKd6rpHi55IcNG8VWYPvISasdGMnjcYr5ct88HgRoTXME8x1EP8PxmprvLfiEiKUAlYIvH4qfdIawXRSSioJ2LyHARSRORtAMH7B4KxktVa8LtqZB5yp2mW/I7/9WOjSR1RBea1Y5mxNtL+XD5Lh8EakzwlNqT4yJSB3gbGKKqOe7iR4HmQEcgDni4oO1V9XVVTVbV5IQE67CYIqjZwpmme2A9TC/5NF2AuCqVmDSsEx0aVuf+qct5e2Hx7g1iTGkQzMSxC6jv8TrRXYaIxACfAH91h7EAUNU97tDWaeAtICWA8ZqK5MIr4YbnYfMXMPvhEk/TBYiODGfC0BSubFaTxz9YzWvzNvsgUGMCL5iJYyYwyJ1d1Rk4oqp7RKQS8D7O+Y/pnhu4vRBERIBeQL4ztozxieQhcMkfYckbsHDU+dt7ITI8lNF3dKBnUl2em72Bpz5ey3ErUWLKGL8VORSRyUBXIF5E0oEngHAAVR0NzAKuBzbjzKQa4m7aB/gNUENE7nSX3enOoJokIgmAAMuBkf6K3xgAfvsk/LwNPvuLU023+fUl3mV4aAgv9kkiJjKcN77dxruLd3BTm7r0S6lPUv1qOL+LjCm9pCLchCY5OVnT0tKCHYYpq86chPE3OOc8hsyCuu18sltV5Yedh5myeAcfrdjDqcxsmteOpl/H+tzcLpHYqHCfvI8xxSUiS1U1+ZzlljiM8cKxffDGVc5tZ+/+EmITfbv7jExmrtjNlMU7WbXrCJXCQri+VW36pTSgU+M464WYoLDEYYnDlNS+tfDmNVC9IQydDRHRfnmb1buOMHXJTj74YRfHTmdxQXwV+nasz60dEomvWuAMdGN8zhKHJQ7jC5u/gEl9oMlVzj09Qv13L7RTZ7L5ZNUepizeQdr2nwkPFa5uWYt+HRtwWZN4QkKsF2L8yxKHJQ7jK0vehE8egJThcP2/A/KWm/YdY8qSncxYls7PJzOpV60yfTvWp09yfWrHRgYkBlPxWOKwxGF86bO/woJXofuz0Dlwk/tOZ2Xz2Zp9TFm8g++3HCREoFuzmvRLaUC3Zgl2z3PjU5Y4LHEYX8rJhtRBsGGWM2TVrLB6nv6x/eAJpi7ZybSl6Rw4dppaMRHc1qE+fTvWp35cVMDjMeWPJQ5LHMbXzpyAt66HnzbB0E+hTtughJGZncNX6/czZfEOvt54gByFy5vG07djfa5pWZtKYdYLMcVjicMSh/GHY3th7FWg2TDsS4itd/5t/Gj34VOkpu0kdclOdh/JIK5KJW5tX49+KQ24MKFqUGMzZY8lDkscxl/2roZx3SGuEQyZDRHB/wOdnaN8s+kAUxbv5It1+8jKUVIaxdEvpT7Xt65DZHhosEM0ZYAlDkscxp82fQ7v9oGm10C/dyGk9Pxh3n8sg/eW7mLqkh38ePAkMZFh3NyuHn07NqBl3Zhgh2dKMUscljiMvy0eC7MehE4j4bpngx3NOVSVBVsPMmXxTmav3suZ7BzaJsbSL6UBN7WtS9UI/12TYsomSxyWOEwgzH4UFr4G1/0bOg0PdjQF+vnEGd7/YRdTluxg477jVKkUyk1t69IvpQFtE2OtxIkBLHFY4jCBkZMNUwfCxtnObWgvujbYERVKVVm2wym0+PHKXwst9k9pQK+kelZosYKzxGGJwwTK6eMw/no4uMWpaVW7dbAj8srRjExmLt/NlCU7WL3rKBFhIVzfug79OtYnxQotVkiWOCxxmEA6useppqvqVNONqRvsiIpk9a4jTFmygw9/2O0UWkyoQr+O9bm1fSI1rNBihWGJwxKHCbS9q9xpuhfAkE9LxTTdojp5JotPVu5hypKdLHULLV7Tsjb9Uupz6YVWaLG8s8RhicMEw8Y5MLkvNL0W+k0qVdN0i2rjvmNMWbyTGT+kc/hkJonVK9M3uT63WaHFcqugxOHXWgQiMk5E9otIvvcGd+83/oqIbBaRlSLS3mPdYBHZ5D4GeyzvICKr3G1eERt4NaXZRdc4hRA3fgpzHgt2NCVyUa1o/nZTSxY+ehUv90uiQVwUz3++kUue+ZJhE5bwxdp9ZGXnBDtMEwD+nrg9HngVmFjA+uuApu6jEzAK6CQicTj3KE8GFFgqIjNV9We3zd3AIpz7lncHPvXjZzCmZDoNh0NbnGm6cRdAyt3BjqhEIsND6ZlUj55J9fjxpxNMTdvJtLR0vliXRq2YCPokO+XerdBi+eX3oSoRaQR8rKqt8lk3BpinqpPd1xuArrkPVR3h2c59zFXV5u7y/p7tCmJDVSbocrJhygDYNAcGpELTq4MdkU9lZufw5br9TFniFFoEuKxJPP06NuDqlrWs0GIZVdBQVbAvFa0H7PR4ne4uK2x5ej7LzyEiw4HhAA0aNPBdxMYUR0go3PomvNUdpt0JQz+D2uf8liqzwkND6N6qNt1b1WbX4VOkLtnJtLSd/O7dZdSoUolbOyTSt2N9K7RYTpTbnwGq+rqqJqtqckJCQrDDMcaZVTUgFSJinLpWR/cEOyK/qFetMv939UV88/CVvDWkI8mNqvPmt9u46vmv6TNmAe//kE5GZnawwzQlEOzEsQuo7/E60V1W2PLEfJYbUzbE1IUBU+HUYWe21ZkTwY7Ib0JDhG7NajLmjmQWPHolf+7ejH1HM/i/qStIefoLnvhwNev2HA12mKYYgp04ZgKD3NlVnYEjqroH+Ay4RkSqi0h14BrgM3fdURHp7M6mGgR8GLTojSmOOm2g9zjnOo9pQ+Do7mBH5Hc1oyO5t2sT5v6pK+8O68QVzWoyefFOrnv5G3r+7zumLN7BidNZwQ7TeMmvJ8dFZDLOie54YB/OTKlwAFUd7f7xfxVnZtRJYIiqprnbDgX+4u7qaVV9y12ejDNbqzLObKo/6Hk+hJ0cN6XS4rEw6yGQEGh+A3QcBo1/AxVkhvmhE2eYsSydKUt2snm/U2ixR1Jd+nVsQBsrtFgq2AWAljhMaXRoK6S9BT+8Dad+hviLIPkuaNsPKlcLdnQB4RRa/JnJi3fy8crdZGTm0KJODP1T6tMzqR6xla3QYrBY4rDEYUqzzFOw5gNY8gbsSoPwKGh9m9MLqdMm2NEFzNGMTD5cvpspi3ewZrdTaPGG1nXol9KAjo2qWy8kwCxxWOIwZcXu5ZD2JqycBlmnIDHFSSAte0J4xSntsSr9CJOX7GDm8t0cP53FhQlV6JVUj5jK4eSokqNOb+XX55Cj6i7jl+Xkee3NNqr6y7q82+So4uxWycnJsw35bJOTZ5t89uu8Vz7vo2dv82tM3m8zdlAyv7moeDNLLXFY4jBlzamfYcUUpxdycDNUjoP2d0DyUKjeKNjRBczJM1l8vHIPUxbvYNmOw0XePkRARM76b4gIISIIzimlkBBxl3m0xeN1iMc2AsLZr0PcNmdtI3m2kXy2KSy2PMvO2gb3/fLbxo0hJMR53Se5+NfPWOKwxGHKKlXY9rWTQNbPAs1xrjzvOAya/LZMF04sqiMnM8lW9eqPc24bU3yl9cpxY8z5iMAFXZ3HkV2wbAIsHe9cRFitgdMDaXcHVIkPapiBYHckLB2sx2FMWZSdCes/hiVvwo/fQGgluPhmpxeS2LHCTOk1/mU9DmPKk9BwJ1FcfDPsX++cTF8+GVZOdW5V23GYMyurUpVgR2rKIetxGFNenD4Oq1KdXsi+1U5NrLb9oeNdkNAs2NGZMshOjlviMBWFKuxc7JxMX/sBZJ+BRpc7vZDmNzi9FWO8YInDEoepiI4fcK5KT3sLjuyAqrWhw53QYbBTcNGYQljisMRhKrKcbNj0udML2fxFha2PZYrGTo4bU5GFhEKz7s7j0DZY+hYsexvWzYQaTZ3zIG37V5j6WKZkrMdhTEWVmeGcA1nyBqQvcetj9XbrY7UNdnSmFLAehzHmbOGRThXetv3Oro+1bKJzLUjHYdCyV4Wqj2W8Yz0OY8yvTh2GFZPPrY/VYQjENQ52dCbA7OS4JQ5jvGf1sQwFJw6/3jpWRLqLyAYR2Swij+SzvqGIfCkiK0Vknogkusu7ichyj0eGiPRy140XkW0e65L8+RmMqZBy62P1fQfuXwVX/Bn2rHTqY72cBN+8ACd+CnaUJkj81uMQkVBgI3A1kA4sAfqr6lqPNtOAj1V1gohciXPr2Dvy7CcO2AwkqupJERnvbjPd21isx2GMD2RnwvpPnF5Ibn2slr2cXkj9FJvSWw4F4+R4CrBZVbe6AUwBegJrPdq0BB5wn88FPshnP72BT1X1pB9jNcacT2g4XNzLeexfD2njnPMhq1KhVmtnSm/r2yCiePd+MGWHP4eq6gE7PV6nu8s8rQBucZ/fDESLSI08bfoBk/Mse9od3npRRCLye3MRGS4iaSKSduDAgeJ9AmNM/mo2h+ufgwfWwY0vAQof3w8vtIBZf4YDG4IdofEjv57j8MKDwBUi8gNwBbALyM5dKSJ1gNbAZx7bPAo0BzoCccDD+e1YVV9X1WRVTU5IKN5tE40x5xFRFZKHwMhvYegcuKi7c3Hh/1Jg/I2w5n1niMuUK/4cqtoF1Pd4negu+4Wq7sbtcYhIVeBWVfW8N2Qf4H1VzfTYZo/79LSIvIWTfIwxwSQCDTo5j+7/cupjLRkH0+5062MNhvaDITbvoIMpi/zZ41gCNBWRxiJSCWfIaaZnAxGJF5HcGB4FxuXZR3/yDFO5vRDEuSdkL2C1H2I3xhRXlXi47P/gvuUwIBXqtIGvn4OXWsPUgbB1njPd15RZfutxqGqWiPweZ5gpFBinqmtE5B9AmqrOBLoC/xIRBeYDv8vdXkQa4fRYvs6z60kikoBzv/jlwEh/fQZjTAmEhMJF1zqPs+pjfWT1sco4uwDQGBM4eetjhVWGNrdB8l1Q1y7JKm2sVpUxJvjOVx8r+S7ndrhWH6tUsx6HMSa4Th2GFVPc+libnPpY7QZC8lCrjxVkVqvKEocxpZsqbJvv1sf6xKmP1eS3zpXpTa+2+lhBYENVxpjSTQQuuMJ5HN0NSyfA0vEwuS/ENnCuF2k3EKokWHmTILMehzGm9MpbHyuXhEBImMcj1M+vg7jNOZ81DEICc+229TiMMWWPZ32sAxtgwyxnZlZOlscju2ivNcdJSJmnir+PoBPvE9JNL0HDS3z67pY4jDFlQ0Iz5xFsqk7yKTTReJOMipGwvGqTJ7ZKvi86aYnDGGOKQgQk1D1Zn2+N1XIv2EUOjTHGlDGWOIwxxhSJJQ5jjDFFYonDGGNMkVjiMMYYUySWOIwxxhSJJQ5jjDFFYonDGGNMkVSIWlUicgDYXszN44GffBiOr1hcRWNxFY3FVTSlNS4oWWwNVTUh78IKkThKQkTS8ivyFWwWV9FYXEVjcRVNaY0L/BObDVUZY4wpEkscxhhjisQSx/m9HuwACmBxFY3FVTQWV9GU1rjAD7HZOQ5jjDFFYj0OY4wxRWKJwxhjTJFY4nCJSHcR2SAim0XkkXzWR4jIVHf9IhFpVEriulNEDojIcvcxLAAxjROR/SKyuoD1IiKvuDGvFJH2/o7Jy7i6isgRj2P1twDFVV9E5orIWhFZIyL35dMm4MfMy7gCfsxEJFJEFovICjeuJ/NpE/Dvo5dxBfz76PHeoSLyg4h8nM863x4vVa3wDyAU2AJcAFQCVgAt87S5FxjtPu8HTC0lcd0JvBrg4/UboD2wuoD11wOfAgJ0BhaVkri6Ah8H4f+vOkB793k0sDGff8eAHzMv4wr4MXOPQVX3eTiwCOicp00wvo/exBXw76PHez8AvJvfv5evj5f1OBwpwGZV3aqqZ4ApQM88bXoCE9zn04GrRERKQVwBp6rzgUOFNOkJTFTHQqCaiNQpBXEFharuUdVl7vNjwDqgXp5mAT9mXsYVcO4xOO6+DHcfeWfxBPz76GVcQSEiicANwBsFNPHp8bLE4agH7PR4nc65X6Bf2qhqFnAEqFEK4gK41R3emC4i9f0ckze8jTsYurhDDZ+KyMWBfnN3iKAdzq9VT0E9ZoXEBUE4Zu6wy3JgP/C5qhZ4vAL4ffQmLgjO9/El4M9ATgHrfXq8LHGUfR8BjVS1DfA5v/6qMOdahlN7py3wX+CDQL65iFQF3gPuV9WjgXzvwpwnrqAcM1XNVtUkIBFIEZFWgXjf8/EiroB/H0XkRmC/qi7193vlssTh2AV4/jJIdJfl20ZEwoBY4GCw41LVg6p62n35BtDBzzF5w5vjGXCqejR3qEFVZwHhIhIfiPcWkXCcP86TVHVGPk2CcszOF1cwj5n7noeBuUD3PKuC8X08b1xB+j5eCvQQkR9xhrOvFJF38rTx6fGyxOFYAjQVkcYiUgnn5NHMPG1mAoPd572Br9Q90xTMuPKMg/fAGacOtpnAIHemUGfgiKruCXZQIlI7d1xXRFJw/v/3+x8b9z3fBNap6gsFNAv4MfMmrmAcMxFJEJFq7vPKwNXA+jzNAv599CauYHwfVfVRVU1U1UY4fyO+UtWBeZr59HiFFXfD8kRVs0Tk98BnODOZxqnqGhH5B5CmqjNxvmBvi8hmnBOw/UpJXH8UkR5AlhvXnf6OS0Qm48y2iReRdOAJnBOFqOpoYBbOLKHNwElgiL9j8jKu3sA9IpIFnAL6BSD5g/OL8A5glTs+DvAXoIFHbME4Zt7EFYxjVgeYICKhOIkqVVU/Dvb30cu4Av59LIg/j5eVHDHGGFMkNlRljDGmSCxxGGOMKRJLHMYYY4rEEocxxpgiscRhjDGmSCxxGFPKiVOh9pyKp8YEiyUOY4wxRWKJwxgfEZGB7v0alovIGLcg3nERedG9f8OXIpLgtk0SkYVuMbz3RaS6u7yJiHzhFhVcJiIXuruv6hbNWy8ikwJQmdmYAlniMMYHRKQF0Be41C2Clw3cDlTBuXr3YuBrnKvZASYCD7vF8FZ5LJ8E/M8tKngJkFt2pB1wP9AS5/4sl/r9QxlTACs5YoxvXIVT0G6J2xmojFN6OweY6rZ5B5ghIrFANVX92l0+AZgmItFAPVV9H0BVMwDc/S1W1XT39XKgEfCt/z+WMeeyxGGMbwgwQVUfPWuhyON52hW3xs9pj+fZ2HfXBJENVRnjG18CvUWkJoCIxIlIQ5zvWG+3zQDgW1U9AvwsIpe7y+8AvnbvwpcuIr3cfUSISFRAP4UxXrBfLcb4gKquFZHHgDkiEgJkAr8DTuDc8OcxnKGrvu4mg4HRbmLYyq/VcO8AxriVTTOB2wL4MYzxilXHNcaPROS4qlYNdhzG+JINVRljjCkS63EYY4wpEutxGGOMKRJLHMYYY4rEEocxxpgiscRhjDGmSCxxGGOMKZL/DzXCcw0dA5LjAAAAAElFTkSuQmCC\n", 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\n" 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