2281 lines
979 KiB
Plaintext
2281 lines
979 KiB
Plaintext
{
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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": "tDybPQiEFQuJ"
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},
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"source": [
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"In this notebook, we will show how to load pre-trained models and draw things with sketch-rnn"
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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": 1,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "k0GqvYgB9JLC"
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/ruben/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
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"/home/ruben/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
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"/home/ruben/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
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"/home/ruben/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
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"/home/ruben/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
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"/home/ruben/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
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"/home/ruben/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
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"/home/ruben/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
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"/home/ruben/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
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"/home/ruben/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
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"/home/ruben/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
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"/home/ruben/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
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]
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}
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],
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"source": [
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"# import the required libraries\n",
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"import numpy as np\n",
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"import time\n",
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"import random\n",
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"import pickle\n",
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"import codecs\n",
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"import collections\n",
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"import os\n",
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"import math\n",
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"import json\n",
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"import tensorflow as tf\n",
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"from six.moves import xrange"
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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": 2,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "UI4ZC__4FQuL"
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},
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"outputs": [],
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"source": [
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"# libraries required for visualisation:\n",
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"from IPython.display import SVG, display\n",
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"import PIL\n",
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"from PIL import Image\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# set numpy output to something sensible\n",
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"np.set_printoptions(precision=8, edgeitems=6, linewidth=200, suppress=True)"
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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": 3,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "D7ObpAUh9jrk"
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},
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"outputs": [],
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"source": [
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"# !pip install -qU svgwrite"
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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": 4,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "4xYY-TUd9aiD"
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},
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"outputs": [],
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"source": [
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"import svgwrite # conda install -c omnia svgwrite=1.1.6"
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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": 5,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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},
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"colab_type": "code",
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"id": "NzPSD-XRFQuP",
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"outputId": "daa0dd33-6d59-4d15-f437-d8ec787c8884"
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},
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"outputs": [],
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"source": [
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"tf.logging.info(\"TensorFlow Version: %s\", tf.__version__)\n"
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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": 6,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "LebxcF4p90OR"
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},
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"outputs": [],
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"source": [
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"# !pip install -q magenta"
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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": 7,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "NkFS0E1zFQuU"
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"WARNING: Logging before flag parsing goes to stderr.\n",
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"W1030 17:41:34.144723 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/pipelines/statistics.py:132: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n",
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"\n",
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"/home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/numba/errors.py:131: UserWarning: Insufficiently recent colorama version found. Numba requires colorama >= 0.3.9\n",
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" warnings.warn(msg)\n",
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"W1030 17:41:35.331515 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/music/note_sequence_io.py:60: The name tf.python_io.TFRecordWriter is deprecated. Please use tf.io.TFRecordWriter instead.\n",
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"\n",
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"W1030 17:41:36.170687 139942895679296 lazy_loader.py:50] \n",
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"The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
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"For more information, please see:\n",
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" * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
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" * https://github.com/tensorflow/addons\n",
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" * https://github.com/tensorflow/io (for I/O related ops)\n",
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"If you depend on functionality not listed there, please file an issue.\n",
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"\n",
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"W1030 17:41:36.172011 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/sketch_rnn_train.py:34: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_verbosity instead.\n",
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"\n",
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"W1030 17:41:36.172474 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/sketch_rnn_train.py:34: The name tf.logging.INFO is deprecated. Please use tf.compat.v1.logging.INFO instead.\n",
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"\n"
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]
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}
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],
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"source": [
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"# import our command line tools\n",
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"from magenta.models.sketch_rnn.sketch_rnn_train import *\n",
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"from magenta.models.sketch_rnn.model import *\n",
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"from magenta.models.sketch_rnn.utils import *\n",
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"from magenta.models.sketch_rnn.rnn import *"
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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": 8,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "GBde4xkEFQuX"
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},
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"outputs": [],
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"source": [
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"# little function that displays vector images and saves them to .svg\n",
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"def draw_strokes(data, factor=0.2, svg_filename = '/tmp/sketch_rnn/svg/sample.svg'):\n",
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" tf.gfile.MakeDirs(os.path.dirname(svg_filename))\n",
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" min_x, max_x, min_y, max_y = get_bounds(data, factor)\n",
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" dims = (50 + max_x - min_x, 50 + max_y - min_y)\n",
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" dwg = svgwrite.Drawing(svg_filename, size=dims)\n",
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" dwg.add(dwg.rect(insert=(0, 0), size=dims,fill='white'))\n",
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" lift_pen = 1\n",
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" abs_x = 25 - min_x \n",
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" abs_y = 25 - min_y\n",
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" p = \"M%s,%s \" % (abs_x, abs_y)\n",
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" command = \"m\"\n",
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" for i in xrange(len(data)):\n",
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" if (lift_pen == 1):\n",
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" command = \"m\"\n",
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" elif (command != \"l\"):\n",
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" command = \"l\"\n",
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" else:\n",
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" command = \"\"\n",
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" x = float(data[i,0])/factor\n",
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" y = float(data[i,1])/factor\n",
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" lift_pen = data[i, 2]\n",
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" p += command+str(x)+\",\"+str(y)+\" \"\n",
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" the_color = \"black\"\n",
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" stroke_width = 1\n",
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" dwg.add(dwg.path(p).stroke(the_color,stroke_width).fill(\"none\"))\n",
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" dwg.save()\n",
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" display(SVG(dwg.tostring()))\n",
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"\n",
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"# generate a 2D grid of many vector drawings\n",
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"def make_grid_svg(s_list, grid_space=10.0, grid_space_x=16.0):\n",
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" def get_start_and_end(x):\n",
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" x = np.array(x)\n",
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" x = x[:, 0:2]\n",
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" x_start = x[0]\n",
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" x_end = x.sum(axis=0)\n",
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" x = x.cumsum(axis=0)\n",
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" x_max = x.max(axis=0)\n",
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" x_min = x.min(axis=0)\n",
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" center_loc = (x_max+x_min)*0.5\n",
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" return x_start-center_loc, x_end\n",
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" x_pos = 0.0\n",
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" y_pos = 0.0\n",
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" result = [[x_pos, y_pos, 1]]\n",
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" for sample in s_list:\n",
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" s = sample[0]\n",
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" grid_loc = sample[1]\n",
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" grid_y = grid_loc[0]*grid_space+grid_space*0.5\n",
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" grid_x = grid_loc[1]*grid_space_x+grid_space_x*0.5\n",
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" start_loc, delta_pos = get_start_and_end(s)\n",
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"\n",
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" loc_x = start_loc[0]\n",
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" loc_y = start_loc[1]\n",
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" new_x_pos = grid_x+loc_x\n",
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" new_y_pos = grid_y+loc_y\n",
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" result.append([new_x_pos-x_pos, new_y_pos-y_pos, 0])\n",
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"\n",
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" result += s.tolist()\n",
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" result[-1][2] = 1\n",
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" x_pos = new_x_pos+delta_pos[0]\n",
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" y_pos = new_y_pos+delta_pos[1]\n",
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" return np.array(result)"
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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": "if7-UyxzFQuY"
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},
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"source": [
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"define the path of the model you want to load, and also the path of the dataset"
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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": 9,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "Dipv1EbsFQuZ"
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},
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"outputs": [],
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"source": [
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"data_dir = 'datasets/naam4'\n",
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"model_dir = 'models/naam4'"
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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": 10,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 68
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},
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"colab_type": "code",
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"id": "eaSqI0fIFQub",
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"outputId": "06df45a6-cc86-4f50-802e-25ae185037f7"
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},
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"outputs": [],
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"source": [
|
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"# download_pretrained_models(models_root_dir=models_root_dir)"
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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": 11,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "G4sRuxyn_1aO"
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},
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"outputs": [],
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"source": [
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"def load_env_compatible(data_dir, model_dir):\n",
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" \"\"\"Loads environment for inference mode, used in jupyter notebook.\"\"\"\n",
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" # modified https://github.com/tensorflow/magenta/blob/master/magenta/models/sketch_rnn/sketch_rnn_train.py\n",
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" # to work with depreciated tf.HParams functionality\n",
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" model_params = sketch_rnn_model.get_default_hparams()\n",
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" with tf.gfile.Open(os.path.join(model_dir, 'model_config.json'), 'r') as f:\n",
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" data = json.load(f)\n",
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" fix_list = ['conditional', 'is_training', 'use_input_dropout', 'use_output_dropout', 'use_recurrent_dropout']\n",
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" for fix in fix_list:\n",
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" data[fix] = (data[fix] == 1)\n",
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" model_params.parse_json(json.dumps(data))\n",
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" return load_dataset(data_dir, model_params, inference_mode=True)\n",
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"\n",
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"def load_model_compatible(model_dir):\n",
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" \"\"\"Loads model for inference mode, used in jupyter notebook.\"\"\"\n",
|
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" # modified https://github.com/tensorflow/magenta/blob/master/magenta/models/sketch_rnn/sketch_rnn_train.py\n",
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" # to work with depreciated tf.HParams functionality\n",
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" model_params = sketch_rnn_model.get_default_hparams()\n",
|
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" with tf.gfile.Open(os.path.join(model_dir, 'model_config.json'), 'r') as f:\n",
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" data = json.load(f)\n",
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" fix_list = ['conditional', 'is_training', 'use_input_dropout', 'use_output_dropout', 'use_recurrent_dropout']\n",
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" for fix in fix_list:\n",
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" data[fix] = (data[fix] == 1)\n",
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" model_params.parse_json(json.dumps(data))\n",
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"\n",
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" model_params.batch_size = 1 # only sample one at a time\n",
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" eval_model_params = sketch_rnn_model.copy_hparams(model_params)\n",
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" eval_model_params.use_input_dropout = 0\n",
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" eval_model_params.use_recurrent_dropout = 0\n",
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" eval_model_params.use_output_dropout = 0\n",
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" eval_model_params.is_training = 0\n",
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" sample_model_params = sketch_rnn_model.copy_hparams(eval_model_params)\n",
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" sample_model_params.max_seq_len = 1 # sample one point at a time\n",
|
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" return [model_params, eval_model_params, sample_model_params]"
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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": 12,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 153
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},
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"colab_type": "code",
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"id": "9m-jSAb3FQuf",
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"outputId": "debc045d-d15a-4b30-f747-fa4bcbd069fd"
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"I1030 17:41:40.760584 139942895679296 sketch_rnn_train.py:142] Loaded 161/161/161 from diede.npz\n",
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"I1030 17:41:40.985831 139942895679296 sketch_rnn_train.py:142] Loaded 100/100/100 from lijn.npz\n",
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"I1030 17:41:41.029351 139942895679296 sketch_rnn_train.py:142] Loaded 100/100/100 from blokletters.npz\n",
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"I1030 17:41:41.234873 139942895679296 sketch_rnn_train.py:159] Dataset combined: 1083 (361/361/361), avg len 234\n",
|
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"I1030 17:41:41.235846 139942895679296 sketch_rnn_train.py:166] model_params.max_seq_len 614.\n",
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"I1030 17:41:41.412048 139942895679296 sketch_rnn_train.py:209] normalizing_scale_factor 55.2581.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
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"total images <= max_seq_len is 361\n",
|
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"total images <= max_seq_len is 361\n",
|
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"total images <= max_seq_len is 361\n"
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]
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}
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],
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"source": [
|
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"[train_set, valid_set, test_set, hps_model, eval_hps_model, sample_hps_model] = load_env_compatible(data_dir, model_dir)"
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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": 13,
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"metadata": {
|
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"colab": {
|
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"base_uri": "https://localhost:8080/",
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"height": 479
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},
|
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"colab_type": "code",
|
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"id": "1pHS8TSgFQui",
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"outputId": "50b0e14d-ff0f-43bf-d996-90e9e6a1491e"
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},
|
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"outputs": [
|
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"W1030 17:41:48.243727 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/sketch_rnn_train.py:62: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
|
|
"\n",
|
|
"W1030 17:41:48.244626 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/sketch_rnn_train.py:65: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.\n",
|
|
"\n",
|
|
"W1030 17:41:48.245686 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/model.py:81: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
|
|
"\n",
|
|
"I1030 17:41:48.254927 139942895679296 model.py:87] Model using gpu.\n",
|
|
"I1030 17:41:48.262013 139942895679296 model.py:175] Input dropout mode = False.\n",
|
|
"I1030 17:41:48.263224 139942895679296 model.py:176] Output dropout mode = False.\n",
|
|
"I1030 17:41:48.263903 139942895679296 model.py:177] Recurrent dropout mode = False.\n",
|
|
"W1030 17:41:48.264627 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/model.py:190: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
|
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"\n",
|
|
"W1030 17:41:48.274967 139942895679296 deprecation.py:323] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/model.py:100: bidirectional_dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
|
|
"Instructions for updating:\n",
|
|
"Please use `keras.layers.Bidirectional(keras.layers.RNN(cell))`, which is equivalent to this API\n",
|
|
"W1030 17:41:48.275926 139942895679296 deprecation.py:323] From /home/ruben/.local/lib/python3.7/site-packages/tensorflow/python/ops/rnn.py:464: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
|
|
"Instructions for updating:\n",
|
|
"Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
|
|
"W1030 17:41:48.364203 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/rnn.py:288: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
|
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"\n",
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"W1030 17:41:48.365810 139942895679296 deprecation.py:506] From /home/ruben/.local/lib/python3.7/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
|
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"Instructions for updating:\n",
|
|
"Call initializer instance with the dtype argument instead of passing it to the constructor\n",
|
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"W1030 17:41:48.573339 139942895679296 deprecation.py:323] From /home/ruben/.local/lib/python3.7/site-packages/tensorflow/python/ops/rnn.py:244: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
|
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"Instructions for updating:\n",
|
|
"Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
|
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"W1030 17:41:49.201451 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/model.py:256: The name tf.nn.xw_plus_b is deprecated. Please use tf.compat.v1.nn.xw_plus_b instead.\n",
|
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"\n",
|
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"W1030 17:41:49.216303 139942895679296 deprecation.py:323] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/model.py:266: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
|
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"Instructions for updating:\n",
|
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"Deprecated in favor of operator or tf.math.divide.\n",
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"W1030 17:41:49.229308 139942895679296 deprecation.py:506] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/model.py:285: calling reduce_sum_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
|
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"Instructions for updating:\n",
|
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"keep_dims is deprecated, use keepdims instead\n",
|
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"W1030 17:41:49.238550 139942895679296 deprecation.py:323] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/model.py:295: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
|
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"Instructions for updating:\n",
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"\n",
|
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"Future major versions of TensorFlow will allow gradients to flow\n",
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"into the labels input on backprop by default.\n",
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"\n",
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"See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
|
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"\n",
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"W1030 17:41:49.286651 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/model.py:351: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
|
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"\n",
|
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"I1030 17:41:51.179355 139942895679296 model.py:87] Model using gpu.\n",
|
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"I1030 17:41:51.179930 139942895679296 model.py:175] Input dropout mode = 0.\n",
|
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"I1030 17:41:51.180556 139942895679296 model.py:176] Output dropout mode = 0.\n",
|
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"I1030 17:41:51.181033 139942895679296 model.py:177] Recurrent dropout mode = 0.\n",
|
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"I1030 17:41:51.579374 139942895679296 model.py:87] Model using gpu.\n",
|
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"I1030 17:41:51.579939 139942895679296 model.py:175] Input dropout mode = 0.\n",
|
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"I1030 17:41:51.580369 139942895679296 model.py:176] Output dropout mode = 0.\n",
|
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"I1030 17:41:51.580825 139942895679296 model.py:177] Recurrent dropout mode = 0.\n"
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]
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}
|
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],
|
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"source": [
|
|
"# construct the sketch-rnn model here:\n",
|
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"reset_graph()\n",
|
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"model = Model(hps_model)\n",
|
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"eval_model = Model(eval_hps_model, reuse=True)\n",
|
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"sample_model = Model(sample_hps_model, reuse=True)"
|
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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": 14,
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"metadata": {
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"colab": {},
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"colab_type": "code",
|
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"id": "1gxYLPTQFQuk"
|
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},
|
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"outputs": [],
|
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"source": [
|
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"sess = tf.InteractiveSession()\n",
|
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"sess.run(tf.global_variables_initializer())"
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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": 15,
|
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"metadata": {
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"colab": {
|
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"base_uri": "https://localhost:8080/",
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"height": 51
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},
|
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"colab_type": "code",
|
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"id": "bVlDyfN_FQum",
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"outputId": "fb41ce20-4c7f-4991-e9f6-559ea9b34a31"
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"W1030 17:42:02.262253 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/sketch_rnn_train.py:239: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.\n",
|
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"\n",
|
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"I1030 17:42:02.329423 139942895679296 sketch_rnn_train.py:241] Loading model models/naam4/vector-4800.\n",
|
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"W1030 17:42:02.330948 139942895679296 deprecation.py:323] From /home/ruben/.local/lib/python3.7/site-packages/tensorflow/python/training/saver.py:1276: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
|
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"Instructions for updating:\n",
|
|
"Use standard file APIs to check for files with this prefix.\n",
|
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"I1030 17:42:02.334095 139942895679296 saver.py:1280] Restoring parameters from models/naam4/vector-4800\n"
|
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]
|
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}
|
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],
|
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"source": [
|
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"# loads the weights from checkpoint into our model\n",
|
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"load_checkpoint(sess, model_dir)\n",
|
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"# saver = tf.train.Saver(tf.global_variables())\n",
|
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"# ckpt = tf.train.get_checkpoint_state(model_dir)\n",
|
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"# print(int(ckpt.model_checkpoint_path.split(\"-\")[-1])/100)\n",
|
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"# # tf.logging.info('Loading model %s.', ckpt.model_checkpoint_path)\n",
|
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"# saver.restore(sess, \"models/naam4/vector-4100\")"
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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": "EOblwpFeFQuq"
|
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},
|
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"source": [
|
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"We define two convenience functions to encode a stroke into a latent vector, and decode from latent vector to stroke."
|
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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": 16,
|
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"metadata": {
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"colab": {},
|
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"colab_type": "code",
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"id": "tMFlV487FQur"
|
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},
|
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"outputs": [],
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"source": [
|
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"def encode(input_strokes):\n",
|
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" strokes = to_big_strokes(input_strokes, 614).tolist()\n",
|
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" strokes.insert(0, [0, 0, 1, 0, 0])\n",
|
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" seq_len = [len(input_strokes)]\n",
|
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" print(seq_len)\n",
|
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" draw_strokes(to_normal_strokes(np.array(strokes)))\n",
|
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" print(np.array([strokes]).shape)\n",
|
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" return sess.run(eval_model.batch_z, feed_dict={eval_model.input_data: [strokes], eval_model.sequence_lengths: seq_len})[0]"
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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": 17,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "1D5CV7ZlFQut"
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},
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"outputs": [],
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"source": [
|
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"def decode(z_input=None, draw_mode=True, temperature=0.1, factor=0.2):\n",
|
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" z = None\n",
|
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" if z_input is not None:\n",
|
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" z = [z_input]\n",
|
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" sample_strokes, m = sample(sess, sample_model, seq_len=eval_model.hps.max_seq_len, temperature=temperature, z=z)\n",
|
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" strokes = to_normal_strokes(sample_strokes)\n",
|
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" if draw_mode:\n",
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" draw_strokes(strokes, factor)\n",
|
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" return strokes"
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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": 23,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 123
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},
|
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"colab_type": "code",
|
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"id": "fUOAvRQtFQuw",
|
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"outputId": "c8e9a1c3-28db-4263-ac67-62ffece1e1e0"
|
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},
|
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"outputs": [
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{
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"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
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"162\n"
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]
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},
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{
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"data": {
|
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"image/svg+xml": [
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|
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-2.53356397151947,0.6333909928798676 l-3.438408374786377,2.352595180273056 -1.4477509260177612,3.1669551134109497 l0.09048443287611008,3.0764704942703247 0.9953287243843079,4.252768158912659 l1.266781985759735,4.252768158912659 1.9001729786396027,3.528892695903778 l2.352595180273056,1.8096886575222015 3.438408374786377,0.9048443287611008 l5.157612562179565,-0.9953287243843079 5.971972346305847,-2.171626389026642 l3.1669551134109497,-0.5429065972566605 0.4524221643805504,-0.3619377315044403 l-1.8096886575222015,-0.6333909928798676 -1.085813194513321,-1.266781985759735 m-89.39861297607422,-28.774049282073975 l-0.27145329862833023,-1.266781985759735 -0.18096886575222015,0.0 l-0.09048443287611008,0.0 0.0,-0.09048443287611008 l0.09048443287611008,-0.18096886575222015 0.09048443287611008,0.0 l0.09048443287611008,0.0 0.0,0.09048443287611008 l0.0,0.09048443287611008 -0.09048443287611008,0.0 l-0.09048443287611008,0.0 0.0,0.0 l0.0,0.09048443287611008 0.27145329862833023,0.5429065972566605 l0.27145329862833023,0.5429065972566605 \" fill=\"none\" stroke=\"black\" stroke-width=\"1\"/></svg>"
|
|
],
|
|
"text/plain": [
|
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"<IPython.core.display.SVG object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# get a sample drawing from the test set, and render it to .svg\n",
|
|
"i = random.randint(101,360)\n",
|
|
"#i=260\n",
|
|
"print(i)\n",
|
|
"stroke = test_set.random_sample()\n",
|
|
"stroke=test_set.strokes[i]\n",
|
|
"draw_strokes(stroke)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "j114Re2JFQuz"
|
|
},
|
|
"source": [
|
|
"Let's try to encode the sample stroke into latent vector $z$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Help on method random_sample in module magenta.models.sketch_rnn.utils:\n",
|
|
"\n",
|
|
"random_sample() method of magenta.models.sketch_rnn.utils.DataLoader instance\n",
|
|
" Return a random sample, in stroke-3 format as used by draw_strokes.\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"help(test_set.random_sample)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 123
|
|
},
|
|
"colab_type": "code",
|
|
"id": "DBRjPBo-FQu0",
|
|
"outputId": "e089dc78-88e3-44c6-ed7e-f1844471f47f"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[181]\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/svg+xml": [
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|
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l0.27145329862833023,0.5429065972566605 \" fill=\"none\" stroke=\"black\" stroke-width=\"1\"/></svg>"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.SVG object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(1, 615, 5)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"z = encode(stroke)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 124
|
|
},
|
|
"colab_type": "code",
|
|
"id": "-37v6eZLFQu5",
|
|
"outputId": "5ddac2f2-5b3b-4cd7-b81f-7a8fa374aa6b"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/svg+xml": [
|
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],
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"text/plain": [
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"<IPython.core.display.SVG object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"_ = decode(z,temperature=1) # convert z back to drawing at temperature of 0.8\n"
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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": "M5ft6IEBFQu9"
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|
},
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|
"source": [
|
|
"Create generated grid at various temperatures from 0.1 to 1.0"
|
|
]
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|
},
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|
{
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 130
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},
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"colab_type": "code",
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"id": "BuhaZI0aFQu9",
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"outputId": "d87d4b00-30c2-4302-bec8-46566ef26922",
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"scrolled": false
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},
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"outputs": [
|
|
{
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|
"name": "stdout",
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"output_type": "stream",
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|
"text": [
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|
"0.1\n"
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]
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},
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{
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"data": {
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"image/svg+xml": [
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|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.SVG object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"for i in range(10):\n",
|
|
" temp = .1 + i*.1\n",
|
|
" print(temp)\n",
|
|
" for j in range(5):\n",
|
|
" stroke = decode(draw_mode=False, temperature=temp)\n",
|
|
" draw_strokes(stroke)\n",
|
|
" \n",
|
|
" \n",
|
|
"# stroke_grid = make_grid_svg(stroke_list)\n",
|
|
"# draw_strokes(stroke_grid)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "4xiwp3_DFQvB"
|
|
},
|
|
"source": [
|
|
"Latent Space Interpolation Example between $z_0$ and $z_1$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 83,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 123
|
|
},
|
|
"colab_type": "code",
|
|
"id": "WSX0uvZTFQvD",
|
|
"outputId": "cd67af4e-5ae6-4327-876e-e1385dadbafc"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/svg+xml": [
|
|
"<svg baseProfile=\"full\" height=\"90.11853251140565\" version=\"1.1\" width=\"183.5966605367139\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:ev=\"http://www.w3.org/2001/xml-events\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><defs/><rect fill=\"white\" height=\"90.11853251140565\" width=\"183.5966605367139\" x=\"0\" y=\"0\"/><path d=\"M38.487485870718956,65.11853251140565 m-6.107860207557678,-2.870575487613678 l-4.996210336685181,-0.7894060015678406 -1.8128877878189087,-2.911415696144104 l-0.5705275386571884,-2.8740668296813965 0.05646933801472187,0.24372059851884842 l0.06725526880472898,0.4540058597922325 0.6718767434358597,0.1635473407804966 l-0.4683074727654457,-1.1790595948696136 0.3687518462538719,0.0568343885242939 l1.102830395102501,-1.7055943608283997 0.4945341870188713,-1.4653514325618744 l0.6107145920395851,0.11514725163578987 1.0401514172554016,-0.9861967712640762 l0.7465182989835739,-2.2748970985412598 0.7058843970298767,-0.06860395427793264 l0.8488832414150238,-3.825756013393402 0.23598186671733856,-0.858948603272438 l0.492764413356781,-4.196976721286774 1.2618504464626312,-5.524801015853882 l1.49126335978508,0.46297427266836166 0.5710553377866745,-0.5914181470870972 l1.324111521244049,-2.869957983493805 1.1529235541820526,-0.7602452486753464 l0.9423540532588959,-0.7763708382844925 1.5857017040252686,-1.7225421965122223 l1.190985068678856,-0.7728572189807892 1.0245110094547272,-0.8566032350063324 l1.3762655854225159,-0.5740072950720787 1.9652363657951355,-0.682358518242836 l2.5619062781333923,-0.2922399342060089 3.602866530418396,-0.18451202660799026 l4.601471722126007,0.33862102776765823 5.727468132972717,0.890490785241127 l6.5982043743133545,1.508084237575531 7.389050126075745,2.2215652465820312 l8.343808054924011,2.5292623043060303 9.627784490585327,3.1690260767936707 l10.69189190864563,3.1827539205551147 12.598700523376465,3.4215247631073 l6.384940147399902,-0.000957229858613573 13.277490139007568,2.1324726939201355 l6.945451498031616,-0.29052194207906723 14.38506007194519,0.9609673917293549 \" fill=\"none\" stroke=\"black\" stroke-width=\"1\"/></svg>"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.SVG object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# get a sample drawing from the test set, and render it to .svg\n",
|
|
"z0 = z\n",
|
|
"_ = decode(z0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 66,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 194
|
|
},
|
|
"colab_type": "code",
|
|
"id": "jQf99TxOFQvH",
|
|
"outputId": "4265bd5f-8c66-494e-b26e-d3ac874d69bb"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[425]\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/svg+xml": [
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],
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"text/plain": [
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"<IPython.core.display.SVG object>"
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]
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"metadata": {},
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}
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],
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"source": [
|
|
"stroke = test_set.random_sample()\n",
|
|
"z1 = encode(stroke)\n",
|
|
"_ = decode(z1)"
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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": "tDqJR8_eFQvK"
|
|
},
|
|
"source": [
|
|
"Now we interpolate between sheep $z_0$ and sheep $z_1$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
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"execution_count": 84,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "_YkPNL5SFQvL"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"z_list = [] # interpolate spherically between z0 and z1\n",
|
|
"N = 10\n",
|
|
"for t in np.linspace(0, 1, N):\n",
|
|
" z_list.append(slerp(z0, z1, t))"
|
|
]
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|
},
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{
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"cell_type": "code",
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"execution_count": 85,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "UoM-W1tQFQvM"
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},
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"outputs": [
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{
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l0.46430084854364395,0.4525650665163994 0.5535252764821053,0.4742274805903435 l0.6478147953748703,0.5045105144381523 0.7171899825334549,0.4743150621652603 l0.8053389191627502,0.43365493416786194 0.902654379606247,0.39929602295160294 l1.0093581676483154,0.3898041322827339 1.5557891130447388,-0.012572596315294504 l2.3213496804237366,-0.011385801481083035 2.0848630368709564,0.009435501415282488 l1.707928478717804,0.0075756077421829104 0.49001075327396393,-0.20971329882740974 l0.2163872867822647,-0.2509132958948612 -0.2258078195154667,-0.01216063741594553 l0.24279573932290077,-0.34297462552785873 \" fill=\"none\" stroke=\"black\" stroke-width=\"1\"/></svg>"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.SVG object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# for every latent vector in z_list, sample a vector image\n",
|
|
"reconstructions = []\n",
|
|
"for i in range(N):\n",
|
|
" reconstructions.append([decode(z_list[i], draw_mode=True), [0, i]])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 122
|
|
},
|
|
"colab_type": "code",
|
|
"id": "mTqmlL6GFQvQ",
|
|
"outputId": "062e015f-29c6-4e77-c6db-e403d5cabd59"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"stroke_grid = make_grid_svg(reconstructions)\n",
|
|
"draw_strokes(stroke_grid)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "vFwPna6uFQvS"
|
|
},
|
|
"source": [
|
|
"Let's load the Flamingo Model, and try Unconditional (Decoder-Only) Generation"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "HH-YclgNFQvT"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model_dir = '/tmp/sketch_rnn/models/flamingo/lstm_uncond'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "-Znvy3KxFQvU"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"[hps_model, eval_hps_model, sample_hps_model] = load_model_compatible(model_dir)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 221
|
|
},
|
|
"colab_type": "code",
|
|
"id": "cqDNK1cYFQvZ",
|
|
"outputId": "d346d57c-f51a-4286-ba55-705bc27d4d0d"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# construct the sketch-rnn model here:\n",
|
|
"reset_graph()\n",
|
|
"model = Model(hps_model)\n",
|
|
"eval_model = Model(eval_hps_model, reuse=True)\n",
|
|
"sample_model = Model(sample_hps_model, reuse=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "7wzerSI6FQvd"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"sess = tf.InteractiveSession()\n",
|
|
"sess.run(tf.global_variables_initializer())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 51
|
|
},
|
|
"colab_type": "code",
|
|
"id": "6mzk8KjOFQvf",
|
|
"outputId": "c450a6c6-22ee-4a58-8451-443462b42d58"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# loads the weights from checkpoint into our model\n",
|
|
"load_checkpoint(sess, model_dir)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "X88CgcyuFQvh"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# randomly unconditionally generate 10 examples\n",
|
|
"N = 10\n",
|
|
"reconstructions = []\n",
|
|
"for i in range(N):\n",
|
|
" reconstructions.append([decode(temperature=0.5, draw_mode=False), [0, i]])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 149
|
|
},
|
|
"colab_type": "code",
|
|
"id": "k57REtd_FQvj",
|
|
"outputId": "8bd69652-9d1d-475e-fc64-f205cf6b9ed1"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"stroke_grid = make_grid_svg(reconstructions)\n",
|
|
"draw_strokes(stroke_grid)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "L-rJ0iUQFQvl"
|
|
},
|
|
"source": [
|
|
"Let's load the owl model, and generate two sketches using two random IID gaussian latent vectors"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "of4SWwGdFQvm"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model_dir = '/tmp/sketch_rnn/models/owl/lstm'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 255
|
|
},
|
|
"colab_type": "code",
|
|
"id": "jJiSZFQeFQvp",
|
|
"outputId": "f84360ca-c2be-482f-db57-41b5ecc05768"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"[hps_model, eval_hps_model, sample_hps_model] = load_model_compatible(model_dir)\n",
|
|
"# construct the sketch-rnn model here:\n",
|
|
"reset_graph()\n",
|
|
"model = Model(hps_model)\n",
|
|
"eval_model = Model(eval_hps_model, reuse=True)\n",
|
|
"sample_model = Model(sample_hps_model, reuse=True)\n",
|
|
"sess = tf.InteractiveSession()\n",
|
|
"sess.run(tf.global_variables_initializer())\n",
|
|
"# loads the weights from checkpoint into our model\n",
|
|
"load_checkpoint(sess, model_dir)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 141
|
|
},
|
|
"colab_type": "code",
|
|
"id": "vR4TDoi5FQvr",
|
|
"outputId": "db08cb2c-952c-4949-d2b0-94c11351264b"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"z_0 = np.random.randn(eval_model.hps.z_size)\n",
|
|
"_ = decode(z_0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 124
|
|
},
|
|
"colab_type": "code",
|
|
"id": "ZX23lTnpFQvt",
|
|
"outputId": "247052f2-a0f3-4046-83d6-d08e0429fafb"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"z_1 = np.random.randn(eval_model.hps.z_size)\n",
|
|
"_ = decode(z_1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "7FjQsF_2FQvv"
|
|
},
|
|
"source": [
|
|
"Let's interpolate between the two owls $z_0$ and $z_1$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "u6G37E8_FQvw"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"z_list = [] # interpolate spherically between z_0 and z_1\n",
|
|
"N = 10\n",
|
|
"for t in np.linspace(0, 1, N):\n",
|
|
" z_list.append(slerp(z_0, z_1, t))\n",
|
|
"# for every latent vector in z_list, sample a vector image\n",
|
|
"reconstructions = []\n",
|
|
"for i in range(N):\n",
|
|
" reconstructions.append([decode(z_list[i], draw_mode=False, temperature=0.1), [0, i]])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 149
|
|
},
|
|
"colab_type": "code",
|
|
"id": "OULjMktmFQvx",
|
|
"outputId": "94b7b68e-9c57-4a1b-b216-83770fa4be81"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"stroke_grid = make_grid_svg(reconstructions)\n",
|
|
"draw_strokes(stroke_grid)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "OiXNC-YsFQv0"
|
|
},
|
|
"source": [
|
|
"Let's load the model trained on both cats and buses! catbus!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "SL7WpDDQFQv0"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model_dir = '/tmp/sketch_rnn/models/catbus/lstm'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 255
|
|
},
|
|
"colab_type": "code",
|
|
"id": "Cvk5WOqHFQv2",
|
|
"outputId": "8081d53d-52d6-4d18-f973-a9dd44c897f2"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"[hps_model, eval_hps_model, sample_hps_model] = load_model_compatible(model_dir)\n",
|
|
"# construct the sketch-rnn model here:\n",
|
|
"reset_graph()\n",
|
|
"model = Model(hps_model)\n",
|
|
"eval_model = Model(eval_hps_model, reuse=True)\n",
|
|
"sample_model = Model(sample_hps_model, reuse=True)\n",
|
|
"sess = tf.InteractiveSession()\n",
|
|
"sess.run(tf.global_variables_initializer())\n",
|
|
"# loads the weights from checkpoint into our model\n",
|
|
"load_checkpoint(sess, model_dir)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 106
|
|
},
|
|
"colab_type": "code",
|
|
"id": "icvlBPVkFQv5",
|
|
"outputId": "f7b415fe-4d65-4b00-c0eb-fb592597dba2"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"z_1 = np.random.randn(eval_model.hps.z_size)\n",
|
|
"z_1 = z\n",
|
|
"_ = decode(z_1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 88
|
|
},
|
|
"colab_type": "code",
|
|
"id": "uaNxd0LuFQv-",
|
|
"outputId": "4de5ee9a-cf14-49f4-e5f5-399a0d0b8215"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"z_0 = np.random.randn(eval_model.hps.z_size)\n",
|
|
"_ = decode(z_0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "VtSYkS6mFQwC"
|
|
},
|
|
"source": [
|
|
"Let's interpolate between a cat and a bus!!!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "qIDYUxBEFQwD"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"z_list = [] # interpolate spherically between z_1 and z_0\n",
|
|
"N = 50\n",
|
|
"for t in np.linspace(0, 1, N):\n",
|
|
" z_list.append(slerp(z_0, z_1, t))\n",
|
|
"# for every latent vector in z_list, sample a vector image\n",
|
|
"reconstructions = []\n",
|
|
"for i in range(N):\n",
|
|
" reconstructions.append([decode(z_list[i], draw_mode=False, temperature=0.15), [0, i]])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 112
|
|
},
|
|
"colab_type": "code",
|
|
"id": "ZHmnSjSaFQwH",
|
|
"outputId": "38fe3c7e-698b-4b19-8851-e7f3ff037744"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"stroke_grid = make_grid_svg(reconstructions)\n",
|
|
"draw_strokes(stroke_grid)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "flZ_OgzCFQwJ"
|
|
},
|
|
"source": [
|
|
"Why stop here? Let's load the model trained on both elephants and pigs!!!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "S8WwK8FPFQwK"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model_dir = '/tmp/sketch_rnn/models/elephantpig/lstm'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 255
|
|
},
|
|
"colab_type": "code",
|
|
"id": "meOH4AFXFQwM",
|
|
"outputId": "764938a7-bbdc-4732-e688-a8a278ab3089"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"[hps_model, eval_hps_model, sample_hps_model] = load_model_compatible(model_dir)\n",
|
|
"# construct the sketch-rnn model here:\n",
|
|
"reset_graph()\n",
|
|
"model = Model(hps_model)\n",
|
|
"eval_model = Model(eval_hps_model, reuse=True)\n",
|
|
"sample_model = Model(sample_hps_model, reuse=True)\n",
|
|
"sess = tf.InteractiveSession()\n",
|
|
"sess.run(tf.global_variables_initializer())\n",
|
|
"# loads the weights from checkpoint into our model\n",
|
|
"load_checkpoint(sess, model_dir)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 121
|
|
},
|
|
"colab_type": "code",
|
|
"id": "foZiiYPdFQwO",
|
|
"outputId": "a09fc4fb-110f-4280-8515-c9b673cb6b90"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"z_0 = np.random.randn(eval_model.hps.z_size)\n",
|
|
"_ = decode(z_0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 163
|
|
},
|
|
"colab_type": "code",
|
|
"id": "6Gaz3QG1FQwS",
|
|
"outputId": "0cfc279c-1c59-419f-86d4-ed74d5e38a26"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"z_1 = np.random.randn(eval_model.hps.z_size)\n",
|
|
"_ = decode(z_1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "oVtr7NnGFQwU"
|
|
},
|
|
"source": [
|
|
"Tribute to an episode of [South Park](https://en.wikipedia.org/wiki/An_Elephant_Makes_Love_to_a_Pig): The interpolation between an Elephant and a Pig"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "lJs9JbROFQwU"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"z_list = [] # interpolate spherically between z_1 and z_0\n",
|
|
"N = 10\n",
|
|
"for t in np.linspace(0, 1, N):\n",
|
|
" z_list.append(slerp(z_0, z_1, t))\n",
|
|
"# for every latent vector in z_list, sample a vector image\n",
|
|
"reconstructions = []\n",
|
|
"for i in range(N):\n",
|
|
" reconstructions.append([decode(z_list[i], draw_mode=False, temperature=0.15), [0, i]])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "0FOuNfJMFQwW"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"stroke_grid = make_grid_svg(reconstructions, grid_space_x=25.0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 130
|
|
},
|
|
"colab_type": "code",
|
|
"id": "bZ6zpdiMFQwX",
|
|
"outputId": "70679bd1-4dba-4c08-b39f-bbde81d22019"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"draw_strokes(stroke_grid, factor=0.3)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "KUgVRGnSFQwa"
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"name": "Sketch_RNN.ipynb",
|
|
"provenance": [],
|
|
"version": "0.3.2"
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "sketchrnn",
|
|
"language": "python",
|
|
"name": "sketchrnn"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.7.4"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 1
|
|
}
|