{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "_dEaVsqSgNyQ"
},
"source": [
"##### Copyright 2021 The TensorFlow Authors."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
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"outputs": [],
"source": [
"#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sT8AyHRMNh41"
},
"source": [
"# Recommend movies for users with TensorFlow Ranking\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8f-reQ11gbLB"
},
"source": [
"In this tutorial, we build a simple two tower ranking model using the [MovieLens 100K dataset](https://grouplens.org/datasets/movielens/100k/) with TF-Ranking. We can use this model to rank and recommend movies for a given user according to their predicted user ratings."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qA00wBE2Ntdm"
},
"source": [
"## Setup\n",
"\n",
"Install and import the TF-Ranking library:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-19T11:34:21.602793Z",
"iopub.status.busy": "2024-03-19T11:34:21.602283Z",
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"shell.execute_reply": "2024-03-19T11:34:49.219735Z"
},
"id": "6yzAaM85Z12D"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
"tf-keras 2.16.0 requires tensorflow<2.17,>=2.16, but you have tensorflow 2.15.1 which is incompatible.\u001b[0m\u001b[31m\r\n",
"\u001b[0m"
]
}
],
"source": [
"!pip install -q tensorflow-ranking\n",
"!pip install -q --upgrade tensorflow-datasets"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-19T11:34:49.225468Z",
"iopub.status.busy": "2024-03-19T11:34:49.225153Z",
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"shell.execute_reply": "2024-03-19T11:34:52.503816Z"
},
"id": "n3oYt3R6Nr9l"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-03-19 11:34:49.704174: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2024-03-19 11:34:49.704225: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2024-03-19 11:34:49.705795: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
]
}
],
"source": [
"from typing import Dict, Tuple\n",
"\n",
"import tensorflow as tf\n",
"\n",
"import tensorflow_datasets as tfds\n",
"import tensorflow_ranking as tfr"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zCxQ1CZcO2wh"
},
"source": [
"## Read the data"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "A0sY6-Rtt_Co"
},
"source": [
"Prepare to train a model by creating a ratings dataset and movies dataset. Use `user_id` as the query input feature, `movie_title` as the document input feature, and `user_rating` as the label to train the ranking model."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-19T11:34:52.509622Z",
"iopub.status.busy": "2024-03-19T11:34:52.509144Z",
"iopub.status.idle": "2024-03-19T11:34:54.215772Z",
"shell.execute_reply": "2024-03-19T11:34:54.214821Z"
},
"id": "M-mxBYjdO5m7"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-03-19 11:34:53.385017: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:274] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n"
]
}
],
"source": [
"%%capture --no-display\n",
"# Ratings data.\n",
"ratings = tfds.load('movielens/100k-ratings', split=\"train\")\n",
"# Features of all the available movies.\n",
"movies = tfds.load('movielens/100k-movies', split=\"train\")\n",
"\n",
"# Select the basic features.\n",
"ratings = ratings.map(lambda x: {\n",
" \"movie_title\": x[\"movie_title\"],\n",
" \"user_id\": x[\"user_id\"],\n",
" \"user_rating\": x[\"user_rating\"]\n",
"})"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5W0HSfmSNCWm"
},
"source": [
"Build vocabularies to convert all user ids and all movie titles into integer indices for embedding layers:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-19T11:34:54.220528Z",
"iopub.status.busy": "2024-03-19T11:34:54.219873Z",
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"shell.execute_reply": "2024-03-19T11:34:56.812268Z"
},
"id": "9I1VTEjHzpfX"
},
"outputs": [],
"source": [
"movies = movies.map(lambda x: x[\"movie_title\"])\n",
"users = ratings.map(lambda x: x[\"user_id\"])\n",
"\n",
"user_ids_vocabulary = tf.keras.layers.experimental.preprocessing.StringLookup(\n",
" mask_token=None)\n",
"user_ids_vocabulary.adapt(users.batch(1000))\n",
"\n",
"movie_titles_vocabulary = tf.keras.layers.experimental.preprocessing.StringLookup(\n",
" mask_token=None)\n",
"movie_titles_vocabulary.adapt(movies.batch(1000))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zMsmoqWTOTKo"
},
"source": [
"Group by `user_id` to form lists for ranking models:\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-19T11:34:56.817807Z",
"iopub.status.busy": "2024-03-19T11:34:56.817133Z",
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"shell.execute_reply": "2024-03-19T11:34:56.853687Z"
},
"id": "lXY7kX7nOSwH"
},
"outputs": [],
"source": [
"key_func = lambda x: user_ids_vocabulary(x[\"user_id\"])\n",
"reduce_func = lambda key, dataset: dataset.batch(100)\n",
"ds_train = ratings.group_by_window(\n",
" key_func=key_func, reduce_func=reduce_func, window_size=100)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-19T11:34:56.858574Z",
"iopub.status.busy": "2024-03-19T11:34:56.857931Z",
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"shell.execute_reply": "2024-03-19T11:34:57.325304Z"
},
"id": "57r87tdQlkcT"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape of movie_title: (100,)\n",
"Example values of movie_title: [b'Man Who Would Be King, The (1975)' b'Silence of the Lambs, The (1991)'\n",
" b'Next Karate Kid, The (1994)' b'2001: A Space Odyssey (1968)'\n",
" b'Usual Suspects, The (1995)']\n",
"\n",
"Shape of user_id: (100,)\n",
"Example values of user_id: [b'405' b'405' b'405' b'405' b'405']\n",
"\n",
"Shape of user_rating: (100,)\n",
"Example values of user_rating: [1. 4. 1. 5. 5.]\n",
"\n"
]
}
],
"source": [
"for x in ds_train.take(1):\n",
" for key, value in x.items():\n",
" print(f\"Shape of {key}: {value.shape}\")\n",
" print(f\"Example values of {key}: {value[:5].numpy()}\")\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YcZJf2qxOeWU"
},
"source": [
"Generate batched features and labels:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-19T11:34:57.330162Z",
"iopub.status.busy": "2024-03-19T11:34:57.329450Z",
"iopub.status.idle": "2024-03-19T11:34:57.381947Z",
"shell.execute_reply": "2024-03-19T11:34:57.381178Z"
},
"id": "ctq2RTOqOfAo"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /tmpfs/tmp/ipykernel_12750/4021484596.py:10: dense_to_ragged_batch (from tensorflow.python.data.experimental.ops.batching) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use `tf.data.Dataset.ragged_batch` instead.\n"
]
}
],
"source": [
"def _features_and_labels(\n",
" x: Dict[str, tf.Tensor]) -> Tuple[Dict[str, tf.Tensor], tf.Tensor]:\n",
" labels = x.pop(\"user_rating\")\n",
" return x, labels\n",
"\n",
"\n",
"ds_train = ds_train.map(_features_and_labels)\n",
"\n",
"ds_train = ds_train.apply(\n",
" tf.data.experimental.dense_to_ragged_batch(batch_size=32))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RJUU3mv-_VdQ"
},
"source": [
"The `user_id` and `movie_title` tensors generated in `ds_train` are of shape `[32, None]`, where the second dimension is 100 in most cases except for the batches when less than 100 items grouped in lists. A model working on ragged tensors is thus used."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-19T11:34:57.385828Z",
"iopub.status.busy": "2024-03-19T11:34:57.385533Z",
"iopub.status.idle": "2024-03-19T11:34:58.410928Z",
"shell.execute_reply": "2024-03-19T11:34:58.409898Z"
},
"id": "GTquqk1GkIfd"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape of movie_title: (32, None)\n",
"Example values of movie_title: [[b'Man Who Would Be King, The (1975)'\n",
" b'Silence of the Lambs, The (1991)' b'Next Karate Kid, The (1994)']\n",
" [b'Flower of My Secret, The (Flor de mi secreto, La) (1995)'\n",
" b'Little Princess, The (1939)' b'Time to Kill, A (1996)']\n",
" [b'Kundun (1997)' b'Scream (1996)' b'Power 98 (1995)']]\n",
"\n",
"Shape of user_id: (32, None)\n",
"Example values of user_id: [[b'405' b'405' b'405']\n",
" [b'655' b'655' b'655']\n",
" [b'13' b'13' b'13']]\n",
"\n",
"Shape of label: (32, None)\n",
"Example values of label: [[1. 4. 1.]\n",
" [3. 3. 3.]\n",
" [5. 1. 1.]]\n"
]
}
],
"source": [
"for x, label in ds_train.take(1):\n",
" for key, value in x.items():\n",
" print(f\"Shape of {key}: {value.shape}\")\n",
" print(f\"Example values of {key}: {value[:3, :3].numpy()}\")\n",
" print()\n",
" print(f\"Shape of label: {label.shape}\")\n",
" print(f\"Example values of label: {label[:3, :3].numpy()}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Lrch6rVBOB9Q"
},
"source": [
"## Define a model\n",
"\n",
"Define a ranking model by inheriting from `tf.keras.Model` and implementing the `call` method:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-19T11:34:58.415106Z",
"iopub.status.busy": "2024-03-19T11:34:58.414364Z",
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"shell.execute_reply": "2024-03-19T11:34:58.419802Z"
},
"id": "e5dNbDZwOIHR"
},
"outputs": [],
"source": [
"class MovieLensRankingModel(tf.keras.Model):\n",
"\n",
" def __init__(self, user_vocab, movie_vocab):\n",
" super().__init__()\n",
"\n",
" # Set up user and movie vocabulary and embedding.\n",
" self.user_vocab = user_vocab\n",
" self.movie_vocab = movie_vocab\n",
" self.user_embed = tf.keras.layers.Embedding(user_vocab.vocabulary_size(),\n",
" 64)\n",
" self.movie_embed = tf.keras.layers.Embedding(movie_vocab.vocabulary_size(),\n",
" 64)\n",
"\n",
" def call(self, features: Dict[str, tf.Tensor]) -> tf.Tensor:\n",
" # Define how the ranking scores are computed: \n",
" # Take the dot-product of the user embeddings with the movie embeddings.\n",
"\n",
" user_embeddings = self.user_embed(self.user_vocab(features[\"user_id\"]))\n",
" movie_embeddings = self.movie_embed(\n",
" self.movie_vocab(features[\"movie_title\"]))\n",
"\n",
" return tf.reduce_sum(user_embeddings * movie_embeddings, axis=2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BMV0HpzmJGWk"
},
"source": [
"Create the model, and then compile it with ranking `tfr.keras.losses` and `tfr.keras.metrics`, which are the core of the TF-Ranking package. \n",
"\n",
"This example uses a ranking-specific **softmax loss**, which is a listwise loss introduced to promote all relevant items in the ranking list with better chances on top of the irrelevant ones. In contrast to the softmax loss in the multi-class classification problem, where only one class is positive and the rest are negative, the TF-Ranking library supports multiple relevant documents in a query list and non-binary relevance labels.\n",
"\n",
"For ranking metrics, this example uses in specific **Normalized Discounted Cumulative Gain (NDCG)** and **Mean Reciprocal Rank (MRR)**, which calculate the user utility of a ranked query list with position discounts. For more details about ranking metrics, review evaluation measures [offline metrics](https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Offline_metrics)."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-19T11:34:58.424051Z",
"iopub.status.busy": "2024-03-19T11:34:58.423563Z",
"iopub.status.idle": "2024-03-19T11:34:58.457080Z",
"shell.execute_reply": "2024-03-19T11:34:58.456368Z"
},
"id": "H2tQDhqkOKf1"
},
"outputs": [],
"source": [
"# Create the ranking model, trained with a ranking loss and evaluated with\n",
"# ranking metrics.\n",
"model = MovieLensRankingModel(user_ids_vocabulary, movie_titles_vocabulary)\n",
"optimizer = tf.keras.optimizers.Adagrad(0.5)\n",
"loss = tfr.keras.losses.get(\n",
" loss=tfr.keras.losses.RankingLossKey.SOFTMAX_LOSS, ragged=True)\n",
"eval_metrics = [\n",
" tfr.keras.metrics.get(key=\"ndcg\", name=\"metric/ndcg\", ragged=True),\n",
" tfr.keras.metrics.get(key=\"mrr\", name=\"metric/mrr\", ragged=True)\n",
"]\n",
"model.compile(optimizer=optimizer, loss=loss, metrics=eval_metrics)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NeBnBFMfVLzP"
},
"source": [
"## Train and evaluate the model\n",
"\n",
"Train the model with `model.fit`."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-19T11:34:58.461501Z",
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"shell.execute_reply": "2024-03-19T11:35:13.415859Z"
},
"id": "bzGm7WqSVNyP"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/3\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" 1/Unknown - 5s 5s/step - loss: 1477.8344 - metric/ndcg: 0.7807 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 2/Unknown - 5s 273ms/step - loss: 1527.9211 - metric/ndcg: 0.7945 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 3/Unknown - 5s 222ms/step - loss: 1564.8604 - metric/ndcg: 0.8016 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 4/Unknown - 5s 200ms/step - loss: 1584.4081 - metric/ndcg: 0.8058 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 5/Unknown - 5s 184ms/step - loss: 1575.7542 - metric/ndcg: 0.8057 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 6/Unknown - 6s 176ms/step - loss: 1579.3936 - metric/ndcg: 0.8049 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 7/Unknown - 6s 167ms/step - loss: 1584.7850 - metric/ndcg: 0.8059 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 8/Unknown - 6s 161ms/step - loss: 1593.7064 - metric/ndcg: 0.8073 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 9/Unknown - 6s 152ms/step - loss: 1590.5203 - metric/ndcg: 0.8075 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 10/Unknown - 6s 148ms/step - loss: 1597.3547 - metric/ndcg: 0.8104 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 11/Unknown - 6s 142ms/step - loss: 1599.4286 - metric/ndcg: 0.8108 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 12/Unknown - 6s 140ms/step - loss: 1604.5621 - metric/ndcg: 0.8123 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 13/Unknown - 6s 141ms/step - loss: 1605.0614 - metric/ndcg: 0.8127 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 14/Unknown - 6s 140ms/step - loss: 1605.7787 - metric/ndcg: 0.8136 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 15/Unknown - 7s 143ms/step - loss: 1606.2090 - metric/ndcg: 0.8142 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 16/Unknown - 7s 140ms/step - loss: 1606.6212 - metric/ndcg: 0.8144 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 17/Unknown - 7s 137ms/step - loss: 1603.9541 - metric/ndcg: 0.8141 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 18/Unknown - 7s 134ms/step - loss: 1601.2360 - metric/ndcg: 0.8149 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 19/Unknown - 7s 131ms/step - loss: 1579.0735 - metric/ndcg: 0.8142 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 30/Unknown - 7s 83ms/step - loss: 1231.8003 - metric/ndcg: 0.8279 - metric/mrr: 1.0000 "
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 39/Unknown - 7s 65ms/step - loss: 1152.8252 - metric/ndcg: 0.8254 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"48/48 [==============================] - 7s 56ms/step - loss: 998.7637 - metric/ndcg: 0.8213 - metric/mrr: 1.0000\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 2/3\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" 1/48 [..............................] - ETA: 52s - loss: 1476.4519 - metric/ndcg: 0.9138 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 2/48 [>.............................] - ETA: 8s - loss: 1526.5396 - metric/ndcg: 0.9113 - metric/mrr: 1.0000 "
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 3/48 [>.............................] - ETA: 7s - loss: 1563.4860 - metric/ndcg: 0.9130 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 4/48 [=>............................] - ETA: 6s - loss: 1583.0378 - metric/ndcg: 0.9161 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 5/48 [==>...........................] - ETA: 6s - loss: 1574.3459 - metric/ndcg: 0.9128 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 6/48 [==>...........................] - ETA: 6s - loss: 1577.8793 - metric/ndcg: 0.9145 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 7/48 [===>..........................] - ETA: 5s - loss: 1583.1188 - metric/ndcg: 0.9151 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 8/48 [====>.........................] - ETA: 5s - loss: 1591.9490 - metric/ndcg: 0.9154 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 9/48 [====>.........................] - ETA: 5s - loss: 1588.6942 - metric/ndcg: 0.9136 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"10/48 [=====>........................] - ETA: 5s - loss: 1595.4871 - metric/ndcg: 0.9148 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"11/48 [=====>........................] - ETA: 5s - loss: 1597.5161 - metric/ndcg: 0.9142 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"12/48 [======>.......................] - ETA: 4s - loss: 1602.5718 - metric/ndcg: 0.9158 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"13/48 [=======>......................] - ETA: 4s - loss: 1603.0027 - metric/ndcg: 0.9161 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"14/48 [=======>......................] - ETA: 4s - loss: 1603.6213 - metric/ndcg: 0.9167 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"15/48 [========>.....................] - ETA: 4s - loss: 1604.0188 - metric/ndcg: 0.9169 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"16/48 [=========>....................] - ETA: 4s - loss: 1604.3984 - metric/ndcg: 0.9169 - metric/mrr: 1.0000"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"17/48 [=========>....................] - ETA: 4s - loss: 1601.7072 - metric/ndcg: 0.9167 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"18/48 [==========>...................] - ETA: 4s - loss: 1598.9357 - metric/ndcg: 0.9160 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"19/48 [==========>...................] - ETA: 3s - loss: 1576.7738 - metric/ndcg: 0.9151 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"30/48 [=================>............] - ETA: 1s - loss: 1229.7483 - metric/ndcg: 0.9149 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"38/48 [======================>.......] - ETA: 0s - loss: 1167.9546 - metric/ndcg: 0.9173 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"48/48 [==============================] - 4s 53ms/step - loss: 997.1824 - metric/ndcg: 0.9161 - metric/mrr: 1.0000\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 3/3\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" 1/48 [..............................] - ETA: 51s - loss: 1470.2378 - metric/ndcg: 0.9325 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 2/48 [>.............................] - ETA: 10s - loss: 1521.0977 - metric/ndcg: 0.9251 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 3/48 [>.............................] - ETA: 8s - loss: 1558.6129 - metric/ndcg: 0.9254 - metric/mrr: 1.0000 "
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 4/48 [=>............................] - ETA: 7s - loss: 1578.3770 - metric/ndcg: 0.9288 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 5/48 [==>...........................] - ETA: 7s - loss: 1569.9446 - metric/ndcg: 0.9263 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 6/48 [==>...........................] - ETA: 6s - loss: 1573.5231 - metric/ndcg: 0.9279 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 7/48 [===>..........................] - ETA: 6s - loss: 1578.7963 - metric/ndcg: 0.9286 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 8/48 [====>.........................] - ETA: 6s - loss: 1587.7528 - metric/ndcg: 0.9300 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 9/48 [====>.........................] - ETA: 5s - loss: 1584.5347 - metric/ndcg: 0.9284 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"10/48 [=====>........................] - ETA: 5s - loss: 1591.4133 - metric/ndcg: 0.9295 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"11/48 [=====>........................] - ETA: 5s - loss: 1593.4933 - metric/ndcg: 0.9291 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"12/48 [======>.......................] - ETA: 5s - loss: 1598.6293 - metric/ndcg: 0.9303 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"13/48 [=======>......................] - ETA: 4s - loss: 1599.0730 - metric/ndcg: 0.9308 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"14/48 [=======>......................] - ETA: 4s - loss: 1599.6893 - metric/ndcg: 0.9315 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"15/48 [========>.....................] - ETA: 4s - loss: 1600.1823 - metric/ndcg: 0.9321 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"16/48 [=========>....................] - ETA: 4s - loss: 1600.5929 - metric/ndcg: 0.9325 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"17/48 [=========>....................] - ETA: 4s - loss: 1597.9266 - metric/ndcg: 0.9320 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"18/48 [==========>...................] - ETA: 4s - loss: 1595.1353 - metric/ndcg: 0.9318 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"19/48 [==========>...................] - ETA: 3s - loss: 1573.0365 - metric/ndcg: 0.9319 - metric/mrr: 1.0000"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"30/48 [=================>............] - ETA: 1s - loss: 1226.9624 - metric/ndcg: 0.9334 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"39/48 [=======================>......] - ETA: 0s - loss: 1148.3666 - metric/ndcg: 0.9364 - metric/mrr: 1.0000"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"48/48 [==============================] - 4s 53ms/step - loss: 994.8384 - metric/ndcg: 0.9383 - metric/mrr: 1.0000\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(ds_train, epochs=3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "V5uuSRXZoOKW"
},
"source": [
"Generate predictions and evaluate."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-19T11:35:13.421260Z",
"iopub.status.busy": "2024-03-19T11:35:13.420612Z",
"iopub.status.idle": "2024-03-19T11:35:13.505324Z",
"shell.execute_reply": "2024-03-19T11:35:13.504340Z"
},
"id": "6Hryvj3cPnvK"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Top 5 recommendations for user 42: [b'Star Wars (1977)' b'Liar Liar (1997)' b'Toy Story (1995)'\n",
" b'Raiders of the Lost Ark (1981)' b'Sound of Music, The (1965)']\n"
]
}
],
"source": [
"# Get movie title candidate list.\n",
"for movie_titles in movies.batch(2000):\n",
" break\n",
"\n",
"# Generate the input for user 42.\n",
"inputs = {\n",
" \"user_id\":\n",
" tf.expand_dims(tf.repeat(\"42\", repeats=movie_titles.shape[0]), axis=0),\n",
" \"movie_title\":\n",
" tf.expand_dims(movie_titles, axis=0)\n",
"}\n",
"\n",
"# Get movie recommendations for user 42.\n",
"scores = model(inputs)\n",
"titles = tfr.utils.sort_by_scores(scores,\n",
" [tf.expand_dims(movie_titles, axis=0)])[0]\n",
"print(f\"Top 5 recommendations for user 42: {titles[0, :5]}\")"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "quickstart.ipynb",
"private_outputs": true,
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
![](\"https://www.tensorflow.org/images/tf_logo_32px.png\"){kind=logo}
View on TensorFlow.org\n",
" ![](\"https://www.tensorflow.org/images/colab_logo_32px.png\"){kind=logo}
Run in Google Colab\n",
" ![](\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\")
View source on GitHub\n",
" ![](\"https://www.tensorflow.org/images/download_logo_32px.png\"){kind=logo}
Download notebook\n",
"