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https://github.com/google-deepmind/deepmind-research.git
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Add board games to physics_planning_games.
PiperOrigin-RevId: 323799306
This commit is contained in:
Mehdi Mirza
committed by
Louise Deason
Louise Deason
parent
a24bda5ed0
commit
c4af01fa75
@@ -0,0 +1,335 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "mP9QIqyCf6G4"
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},
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"source": [
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"Copyright 2020 DeepMind Technologies Limited.\n",
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"\n",
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"Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
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"\n",
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"https://www.apache.org/licenses/LICENSE-2.0\n",
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"\n",
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"Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "ng3jUYyQgSjB"
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},
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"source": [
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"# The dataset used for the Paired associate inference task\n",
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"\n",
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"This is the dataset used for the paired associated inference task in\n",
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"[\"MEMO: A Deep Network for Flexible Combination of Episodic Memories\n",
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"\"](https://arxiv.org/abs/2001.10913)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "2Nd8cdyccWld"
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},
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"outputs": [],
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"source": [
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"from __future__ import absolute_import\n",
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"from __future__ import division\n",
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"from __future__ import print_function\n",
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"\n",
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"import numpy as np\n",
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"\n",
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"import tensorflow as tf\n",
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"import collections\n",
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"import os\n",
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"\n",
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"from google.colab import auth\n",
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"auth.authenticate_user()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "GP0u6GCUF_6R"
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},
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"outputs": [],
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"source": [
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"#@title Choices about the dataset you want to load.\n",
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"# Make choices about the dataset here.\n",
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"chain_length = 3 #@param {type:\"slider\", min:3, max:4, step:1}\n",
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"mode = 'valid' #@param ['train', 'test', 'valid']"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "JUWNNIwziHyC"
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},
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"source": [
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"**If you choose chain_length 3 the data will look like this:**\n",
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"\n",
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"* trials shape: (48, 3, 1000); 48 trials x the target picture, left and right option x picture dimensions.\n",
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"* correct answer: (48); whether the left or right picture is correct.\n",
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"* difficulty (48); How far apart are the target picture and the two options.(e.g. AB are 0 steps apart, AC is 1)\n",
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"* trial type (48); See below.\n",
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"* memory shape (32, 2, 1000); Content of memory store, 32 pairs of images.\n",
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"\n",
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"Trial types:\n",
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"* 1: AB\n",
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"* 2: BC\n",
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"* 3: AC\n",
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"\n",
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"\n",
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"**If you choose chain_length 4 the data will look like this:**\n",
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"* trials: (96, 3, 1000)\n",
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"* correct answer: (96)\n",
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"* difficulty: (96)\n",
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"* trial type: (96)\n",
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"* memory shape: (48, 2, 1000)\n",
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"\n",
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"Trial types:\n",
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"* 1: AB\n",
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"* 2: BC\n",
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"* 3: AC\n",
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"* 4: CD\n",
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"* 5: BD\n",
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"* 6: AD"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "0QETPFeEgr5d"
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},
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"outputs": [],
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"source": [
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"# Train has 500 shards, valid 150, test 100.\n",
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"if mode == 'train':\n",
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" num_shards = 500\n",
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"elif mode == 'test':\n",
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" num_shards = 100\n",
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"elif mode == 'valid':\n",
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" num_shards = 150"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "jE3_9k8DOMyZ"
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},
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"outputs": [],
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"source": [
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"DatasetInfo = collections.namedtuple(\n",
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" 'DatasetInfo',\n",
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" ['basepath', 'size', 'chain_length']\n",
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")\n",
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"\n",
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"_DATASETS = dict(\n",
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" memo=DatasetInfo(\n",
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" basepath=mode,\n",
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" size=num_shards,\n",
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" chain_length=chain_length)\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "N3D11lxl3kjF"
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},
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"outputs": [],
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"source": [
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"def _get_dataset_files(dataset_info, root):\n",
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" \"\"\"Generates lists of files for a given dataset version.\"\"\"\n",
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" basepath = dataset_info.basepath\n",
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" base = os.path.join(root, basepath)\n",
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" num_files = dataset_info.size\n",
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" length = len(str(num_files))\n",
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" template = 'trials-{:0%d}-of-{:0%d}' % (5, 5)\n",
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" return [os.path.join(base, template.format(i, num_files))\n",
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" for i in range(num_files)]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "6yqLJYAnsyZF"
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},
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"outputs": [],
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"source": [
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"def parser_tf_examples(raw_data, chain_length=chain_length):\n",
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" if chain_length == 3:\n",
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" feature_map = {\n",
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" 'trials' : tf.io.FixedLenFeature(\n",
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" shape=[48, 3, 1000],\n",
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" dtype=tf.float32),\n",
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" 'correct_answer': tf.io.FixedLenFeature(\n",
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" shape=[48],\n",
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" dtype=tf.int64),\n",
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" 'difficulty': tf.io.FixedLenFeature(\n",
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" shape=[48],\n",
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" dtype=tf.int64),\n",
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" 'trial_type': tf.io.FixedLenFeature(\n",
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" shape=[48],\n",
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" dtype=tf.int64),\n",
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" 'memory': tf.io.FixedLenFeature(\n",
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" shape=[32, 2, 1000],\n",
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" dtype=tf.float32),\n",
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" }\n",
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" elif chain_length == 4: \n",
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" feature_map = {\n",
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" 'trials' : tf.io.FixedLenFeature(\n",
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" shape=[96, 3, 1000],\n",
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" dtype=tf.float32),\n",
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" 'correct_answer': tf.io.FixedLenFeature(\n",
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" shape=[96],\n",
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" dtype=tf.int64),\n",
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" 'difficulty': tf.io.FixedLenFeature(\n",
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" shape=[96],\n",
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" dtype=tf.int64),\n",
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" 'trial_type': tf.io.FixedLenFeature(\n",
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" shape=[96],\n",
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" dtype=tf.int64),\n",
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" 'memory': tf.io.FixedLenFeature(\n",
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" shape=[48, 2, 1000],\n",
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" dtype=tf.float32),\n",
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" }\n",
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" example = tf.io.parse_example(raw_data, feature_map)\n",
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" batch = [example[\"trials\"],\n",
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" example[\"correct_answer\"],\n",
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" example[\"difficulty\"],\n",
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" example[\"trial_type\"],\n",
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" example[\"memory\"]]\n",
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" return batch"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "nXMhOoHWj0oP"
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},
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"source": [
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"## Load the data."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "rXIOBlWKyMY0"
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},
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"outputs": [],
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"source": [
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"dataset_info = 'memo'\n",
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"root = 'gs://deepmind-memo/length' + str(chain_length) + '/'\n",
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"num_epochs = 100\n",
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"shuffle_buffer_size = 150\n",
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"num_readers = 4\n",
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"dataset_info = _DATASETS['memo']\n",
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"filenames = _get_dataset_files(dataset_info, root)\n",
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"num_map_threads = 4\n",
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"batch_size = 10"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "e2G5MSVf9Hpm"
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},
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"outputs": [],
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"source": [
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"data = tf.data.Dataset.from_tensor_slices(filenames)\n",
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"data = data.repeat(num_epochs)\n",
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"data = data.shuffle(shuffle_buffer_size)\n",
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"data = data.interleave(tf.data.TFRecordDataset,\n",
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" cycle_length=num_readers, block_length=1)\n",
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"data = data.shuffle(shuffle_buffer_size)\n",
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"data = data.map(parser_tf_examples, num_parallel_calls=num_map_threads)\n",
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"data = data.batch(batch_size)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "1z3dsDNqkBHD"
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},
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"source": [
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"# Looking at what we loaded."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "ibVadDeeAU4Q"
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},
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"outputs": [],
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"source": [
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"iterator = data.__iter__()\n",
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"element = iterator.get_next()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "Lh7-f08nAeGq"
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},
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"outputs": [],
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"source": [
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"print(element[0].shape) # trials\n",
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"print(element[1].shape) # correct answer\n",
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"print(element[2].shape) # difficulty\n",
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"print(element[3].shape) # trialtype\n",
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"print(element[4].shape) # memory"
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]
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}
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],
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"metadata": {
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"colab": {
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"collapsed_sections": [],
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"name": "load_memo_data.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"name": "python3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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